The SStructure oof aa SSocial SScience Collaboration NNetwork: …jmoody77/asr_soccoauth.pdf · 2006-04-24 · social science collaboration network over time, and I link this structure

Science, carved up into a host of detailed stud-ies that have no link with one another, no longerforms a solid whole.

Durkheim, 1933 [1984] p. 294

Recent work in the sociology of knowledge|suggests that the set of ideas one holds to

be true is largely a function of the group ofpeople one interacts with and references toauthorities recognized by the group. This claimhas been demonstrated in small groups (Martin2002) and is consistent with literature on thesocial production of scientif ic knowledge(Babchuk et al. 1999; Crane 1972; Friedkin1998; Kuhn 1970). Scientists embedded in col-laboration networks share ideas, use similartechniques, and otherwise influence each other’swork. Such effects have been studied in specif-ic settings (Friedkin 1998) and implicated in labethnographies (Collins 1998; Owen-Smith2001), but this social interaction structure hasnot been explored for entire disciplines.Although we might expect the link betweennetworks and ideas to be strongest in smallgroups, a logical extension suggests that long-term trends in scientific work might depend onthe broader pattern of disciplinary social net-works.1

TThhee SSttrruuccttuurree ooff aa SSoocciiaall SScciieennccee CCoollllaabboorraattiioonn NNeettwwoorrkk:: DDiisscciipplliinnaarryy CCoohheessiioonn ffrroomm 11996633 ttoo 11999999

James MoodyThe Ohio State University

Has sociology become more socially integrated over the last 30 years? Recent work in

the sociology of knowledge demonstrates a direct linkage between social interaction

patterns and the structure of ideas, suggesting that scientific collaboration networks

affect scientific practice. I test three competing models for sociological collaboration

networks and find that a structurally cohesive core that has been growing steadily since

the early 1960s characterizes the discipline’s coauthorship network. The results show

that participation in the sociology collaboration network depends on research specialty

and that quantitative work is more likely to be coauthored than non-quantitative work.

However, structural embeddedness within the network core given collaboration is largely

unrelated to specialty area. This pattern is consistent with a loosely overlapping

specialty structure that has potentially integrative implications for theoretical

development in sociology.

AAMMEERRIICCAANN SSOOCCIIOOLLOOGGIICCAALL RREEVVIIEEWW,, 22000044,, VVOOLL.. 6699 ((AApprriill::221133––223388))

#1471-ASR 69:2 filename:69204-moody

Direct correspondence to James Moody, The OhioState University, Department of Sociology, 372Bricker Hall, 190 N. Oval Mall, Columbus, OH,43210 ([email protected]). This work issupported in part by NSF ITR/SOC-0080860 andthe Ohio State Initiative in Population Research. Theauthor thanks jimi adams and Sara Bradley for helpwith data collection and manuscript preparation; andMark Handcock, Mark Newman, and Paul von Hipplefor help with data or analysis. The author thanks thefollowing people for reading prior versions of thepaper and providing helpful comments: Art Alderson,Susanne Bunn, Ben Cornwell, Bill Form, DavidJacobs, Lisa Keister, Dan Lichter, Dan McFarland,Jason Owen-Smith, Woody Powell, and the partici-pants of the Stanford University SCANCOR semi-nar and the University of Chicago Business SchoolOrganizations and Markets Workshop. The authorthanks the ASR reviewers and editors (Camic andWilson) for being extremely helpful; and reviewerJohn A. Stewart, in particular, for providing numer-ous suggestions that improved this paper.

1 Much literature on citation networks suggestssimilar subgroup effects and captures one facet of the

Commentaries on sociology often describe alack of theoretical consensus without referenceto social cohesion, though the two should belinked because structural cohesion is thought togenerate coherent idea systems (Durkheim[1933] 1984; Hagstrom 1965; Hargens 1975;Martin 2002; Moody and White 2003; Whitley2000). A network influence model suggests thatif scientists exchange ideas, research questions,methods, and implicit rules for evaluating evi-dence with their collaborators, then structural-ly cohesive social networks should generateconsensus, at least with respect to problemsand methods if not on particular claims aboutthe empirical world (Friedkin 1998).2 This worksuggests that understanding theoretical diversitywithin a discipline requires understanding itscollaboration structure.

While a direct mapping from the idea spaceto network structure is often not transparent,claims about theoretical consensus in sociologysuggest three distinct collaboration structures.First, many have noted that the discipline has nooverarching theory but, instead, is theoreticallyfractured and composed of multiple discon-nected research specialties. Authors argue thatreactions against functionalism, rapid growth,institutional pressures for productivity, changingresearch techniques, and/or changes in the fund-ing environment for social sciences have inter-acted to generate self-contained researchspecialties, with unique research techniques andstandards for the evaluation of evidence (Collins2001; Davis 2001; Lieberson and Lynn 2002;Stinchcombe 2001). This description suggests ahighly clustered social network. Second, othershave argued that scientific production dependscrucially on a few scientific stars, whose work

shapes the short-run course of a discipline(Allison et al. 1982; Cole and Cole 1973; Merton1968; Zuckerman 1977; see also Crane 1972).Scientific stars attract a disproportionate level ofresearch funding, high-profile appointments,and many students and collaborators. Star sys-tems suggest an unequal distribution of involve-ment in collaboration networks. Finally, changesin research practice might interact with perme-able theoretical boundaries to allow wide-rang-ing collaborations that are not constrained byresearch specialty (Abbott 2001; Hudson 1996).For example, an increase in sophisticated quan-titative methods, which are (at least on the sur-face) substantively neutral, would allowcollaboration between people with general tech-nical skills and those working on particularempirical questions. This process suggests awide-reaching structurally cohesive collabora-tion network.

In this paper, I describe the structure of asocial science collaboration network over time,and I link this structure to claims about social sci-entific practice.3 I first review literature on net-work structure and idea spaces, and link threedescriptions of the current state of sociologicalpractice to hypotheses about the structure of thenetwork. I then describe collaboration trendsand examine explanations for increasing col-laboration over time. After describing the datasource and measures, I first model participationin the collaboration network and then modelposition within the network given participation.I note two key findings. First, specialty areas dif-fer in the likelihood of collaboration, and much(but not all) of this difference is due to use ofquantitative methods. Second, the resulting col-laboration network has a large structurally cohe-sive core that has been growing steadily since thelate 1960s (both absolutely and relative to ran-dom expectation given growth in the discipline).While research specialty predicts having col-laborated, specialty is only weakly related toposition within the collaboration network, sug-gesting a relatively equal representation of spe-cialties across the disciplinary network.

221144——––AAMMEERRIICCAANN SSOOCCIIOOLLOOGGIICCAALL RREEVVIIEEWW


intellectual integration of scientific disciplines (Craneand Small 1992; Hargens 2000). Citation networksare not social networks, however, and thus do not cap-ture the informal interaction structure described inwork on social integration. Recent work has lookedat the global structure of large-scale collaboration net-works in the natural sciences (Newman 2001), but hasnot attempted to explain the features of such networkswith respect to scientific practice.

2 That is, scientific competition for distinctionshould produce a race toward new empirical expla-nations, but to gain status within a field those claimsmust conform to the general rules of evidence cur-rent within the scientific network.

3 While the majority of all authors in the data I useare sociologists, the database does cover sociologi-cally relevant work produced by other disciplines. Assuch, while it is technically more accurate to speakof “a social science” collaboration network, I willoften use “sociology” for simplicity.

SSOOCCIIAALL AANNDD TTHHEEOORREETTIICCAALLIINNTTEEGGRRAATTIIOONN

NNEETTWWOORRKK SSTTRRUUCCTTUURREESS AANNDD IIDDEEAA SSPPAACCEESS

There is increasing interest in linking the dis-tribution of cultural ideas and practices to theinteraction structure of social communities(Bearman 1993; Burt 1987; Crane 1972; Martin2002; Swidler and Arditi 1994). Theorists havelong argued that one’s ideas are a function ofposition in a social setting, which is deeplystructured by interaction patterns (Durkheim[1933] 1984; Mannheim 1936; Simmel 1950).Kuhn (1970), for example, argued that belief inthe empirical validity of theory could be sus-tained long past the available empirical evi-dence if scientists were embedded in researchcommunities who systematically interpreteddata in similar ways. This implicit perspectivewas made clear in Crane’s (1972) work linkingthe rapid development of new ideas to the socialstructure of small “invisible colleges.” Cranefound that research specialties were character-ized by a core group of scientists who collabo-rated with each other and generated adisproportionate volume of new ideas.

Recent work has built on these ideas to direct-ly link network structure to the distribution ofideas. Martin (2002) argues that while predict-ing the specific content of ideas is often not pos-sible, we can link the shape of an idea space tothe structure of a network. In the small groupsthat Martin studied, belief consensus dependedon the authority structure within the group’ssocial network. Similar work on ideational dif-fusion suggests that people influence the beliefsof their social contacts. As part of a broader proj-ect devoted to understanding the distributionof ideas, Friedkin (1998) shows that agreementacross different groups of scientists dependson loosely overlapping cohesive groups in theunderlying social network. Thus, whileFriedkin’s mechanism differs from Martin’s(interpersonal influence as opposed to hierar-chical authority), the general point is quite clear:Belief consensus depends critically on the shapeof the underlying social network.4 If this workis correct, then we can draw hypotheses aboutthe structure of interaction networks in the social

sciences from descriptions of scientific practiceand speculate about the potential for scientificconsensus in a field based on the observed col-laboration pattern.

TTHHEEOORRYY AANNDD PPRRAACCTTIICCEE IINN SSOOCCIIAALL SSCCIIEENNCCEE

THEORETICAL FRAGMENTATION. There is much lit-erature on the lack of theoretical consensus insociology (Abbott 2000; Collins 1986; Connell2000; Davis 2001).5 For example, Stinchcombe(2001) argues that sociology likely has a dimfuture, because “[first] it is unlikely to developmuch consensus on who best represents thesociologists’ sociologist to be hired in elitedepartments. Second, …, it is unlikely to beable to argue with one voice about what is ‘ele-mentary’” (p. 86). This problem is compound-ed by multiple empirical specialties in thediscipline. “The wide variety of substantivesubject matter in disintegrated disciplines, andthe strong boundaries around substantive spe-cialties, means that people cannot get interest-ed in each other’s work.” (p.89), and manyauthors comment on this basic state of intel-lectual anomie.

Sociology’s rapid growth also contributes toperceptions of fractionalization. Simspon andSimpson (2001) report a nearly 5-fold increasein ASA membership since the 1950s, and a risein the number of ASA sections (5 in 1961, 25in 1987, 44 in 2003). However, fears about dis-ciplinary fractionalization based on number ofsections and multitudes of topics risk mistakinggrowth on the margins for separation. Whenviewed as a potential mixing space defined bythe intersection of research areas (Crane andSmall 1992; Daipha 2001; Ennis 1992), simpleincreases in the number of sections tell us littleabout how people mix across these areas.

Visions of a theoretically fractured social sci-ence suggest a highly clustered social network.If substantive boundaries mean that people arenot interested in each other’s work, then peopleshould turn to fellow specialists as potential

SSTTRRUUCCTTUURREE OOFF SSOOCCIIAALL SSCCIIEENNCCEE CCOOLLLLAABBOORRAATTIIOONN NNEETTWWOORRKK——––221155


4 There is much literature within social networkson the various mechanisms that generate consensusfrom networks (see Burt 1987 for a review).

5 Not all of this literature is negative, and severalpeople comment on what is good about the discipline,including the diversity of ideas and interest of top-ics, and many attempts are made at theoretical inte-gration or reformulations (cf Lieberson and Lynn2002; Skvoretz 1998).

collaborators. Graduate students will be trainedwithin particular specialties and a shop-pro-duction model should build distinct communi-ties surrounding particular topics. The resultingnetwork will admit to clear clusters with littlecollaboration crossing specialty boundaries.

The social network model that best fits thisdescription is the small-world model (Milgram1969; Watts 1999; Watts and Strogatz 1998).Intuitively, a small-world network is any networkwhere the level of local clustering (one’s col-laborators are also collaborators with each other)is high, but the average number of steps betweenactors is small. An archetypical small-worldnetwork will have many distinct clusters, con-nected to each other by a small number of links.Distinct research clusters will likely inhibitbroad theoretical integration, since theory willprogress largely within distinct research groups.

STAR PRODUCTION. Past research on stratifi-cation in the sciences has identif ied largeinequality in the returns to scientific labor(Allison, Long and Krauze 1982; Cole and Cole1973; Merton 1968). Although most scientistslabor in obscurity, a small number of scientistsreceive disproportionate recognition. This hasbeen clearly demonstrated for indicators such ascitations, number of publications, or grants.However, research suggests that collaborationis also unequally divided. Crane (1972) foundthat a small number of very prominent scientistsform the core of each specialty’s collaborationnetwork and that most others were connected tothe rest of the community through these high-ly active individuals. This central position helpsexplain why core scientists were able to so rap-idly diffuse their ideas through the community,and we would expect that those with many col-laborators are likely to be influential (at leastlocally). Newman (2001) turns collaborationitself into a status marker and asks, “Who is theBest Connected Scientist?”6

The large inequality in numbers of collabo-rators can be explained through a process ofpreferential attachment. High-status scientistsmake attractive collaborators since one’s own

status is a function of the status of those towhom one is connected (Bonacich 1987; Gould2002; Leifer 1988). This implies that peoplewill seek to work with high-status scientists, andthis process will be self-reinforcing. The typi-cal preferential attachment process suggeststhat as new people enter the network, they col-laborate with those already in the network withprobability proportional to their current numberof partners. The critical structural feature for thepreferential attachment model is that star actorsare responsible for connecting the network.

The network model that best fits a star pro-duction process is a scale-free model (Barabasiand Albert 1999; Newman 2000). Barabasi(1999) proved that when networks are con-structed through a preferential attachmentprocess, the resulting distribution of the num-ber of unique collaborators (called the degreedistribution) will have a scale-free power-lawdistribution, such that the probability of havingk partners is distributed as k–�, and we can thususe the degree distribution to test for a prefer-ential attachment process.7 Theoretical inte-gration in such networks will likely dependcrucially on ideas generated by star producers,as collaborators follow the lead of those respon-sible for connecting the entire network.

PERMEABLE THEORETICAL BOUNDARIES AND

GENERIC METHODS. Abbott (2001) also arguesthat the social sciences have little theoreticalconsensus, but he does not suggest that thisgenerates a clustered discipline. Instead, he sug-gests that the nature of sociology creates per-meable theoretical boundaries that make itimpossible for sociology to exclude ideas fromthe discipline once they are introduced (p. 6; seealso Daipha 2001). Moreover, the process of the-oretical development is not linear, but insteadfollows a “fractal walk” through the availableidea space. Pushed by competition for status,proponents of one set of ideas attempt to van-



6 Competition among mathematicians over havingthe smallest Erdös number speaks similarly to the sta-tus attached to one’s position in a collaboration net-work.

7 Not finding a power-law distribution will falsi-fy a preferential attachment model, but the oppositedirection does not hold: finding a power-law distri-bution is consistent with preferential attachment, butother processes can also generate power-law distri-butions. This asymmetry has lead to some misun-derstandings in the current literature over large-scalenetworks.

quish another, only to find that they need toreinvent those same ideas later. This results ina constant revisiting of ideas and interests in thediscipline (though usually from a different direc-tion) as actors continuously loop through widesections of the available idea space.

Permeability allows for cross-topic collabo-ration, since the same theoretical frame (eco-logical, competition, diffusion throughnetworks, etc.) can be applied to multiple empir-ical questions. This implies that while peoplemight specialize in techniques or approaches,these techniques and approaches are transferableacross research questions. If this is the case,then the boundless character of sociology wouldpromote wide-ranging collaboration. Authorswith particular technical, empirical or theoret-ical skills will mix freely with those who haveworked in different research areas, in an attemptto establish a new position by combining pre-vious work. If many engage in this kind ofcross-fertilization, mixing across multiple areas,the result will be a social structure with few cleardivisions.

The social network model that best fits thisdescription is structural cohesion (White andHarary 2001; Moody and White 2003; White etal. 2002). A network is structurally cohesivewhen ties are distributed evenly across the net-work, implying no clear fissures in the under-lying structure (Markovsky 1998). Moody andWhite (2003) show that this network featurecan be exactly characterized as the extent towhich a network will remain connected whennodes are removed from the network. Such net-works are, topologically, the structural oppositeof those implied by a preferential attachmentprocess. In preferential attachment networks,most relational paths pass through highly activenodes, which if removed would disconnect thenetwork. This key structural location gives staractors unique control over the spread of ideasin a network. In structurally cohesive networks,in contrast, ties are distributed such that stars inthe network are not crucial for connecting thenetwork, and ideas are more likely to spread overthe entire network. A structurally cohesive net-work suggests increasing theoretical integra-tion, at least within the multiply-connected corecollaboration network.

The three models for large-scale networkscorrespond theoretically to expectations aboutsocial scientific production. If authors in well-

defined research specialties collaborate witheach other, then we would expect to find distinctclusters in the knowledge production network,which corresponds to a small-world networkstructure. If the network was generated by pref-erential attachment, where young authors writewith well-established stars, then we wouldexpect to find a scale-free network structure. Ifthe multiple theoretical perspectives found in thesocial sciences admit to permeable boundariesallowing specialists to mix freely, then we wouldexpect no strong fissures in the network, butinstead find a structurally cohesive network.Before looking at the structure of the coau-thorship network, we need to first examine thetrends and determinants of coauthorship.

SSCCIIEENNTTIIFFIICC CCOOLLLLAABBOORRAATTIIOONN TTRREENNDDSS

The probability of coauthoring differs acrossdisciplines and over time. Coauthorship is morecommon in the natural sciences than in thesocial sciences, but has been increasing steadi-ly across all fields (Endersby 1996; Fisher et al.1998; Hargens 1975; Laband and Tollison2000). The changing likelihood of coauthor-ship is evident in Figure 1, which shows the pro-portion of all articles coauthored in ASR frominception and in Sociological Abstracts from1963 to 1999.

Several explanations have been given for theincrease in coauthorship over time (Laband andTollison 2000; McDowell and Michael 1983).Funding requirements, particularly in large labsettings, might induce collaboration (Labandand Tollison 2000; Zuckerman and Merton1973). While social scientists are rarely asdependent on labs, the rise of large-scale datacollection efforts suggests a similar team-pro-duction model. Training differences betweendisciplines might also account for coauthorshipdifferences. Advanced work by PhD students inthe natural sciences is usually closely related toan advisor’s work, and commonly results in col-laboration. Social science students, in contrast,tend to work on projects that are more inde-pendent.

Other explanations focus on the division oflabor among scientists. In high-growth, fast-changing specialties, we would expect to seemore coauthorship because it is easier to bringin a new author than it is to learn new materialoneself. Hudson (1996) argues that the increase



in coauthorship in economics is due to the risein quantitative methods. As quantitative tech-niques become more complicated, specialists areoften added to research teams to do the analy-ses. These points are well supported by thelower rates of coauthorship among theoreticalor historical specialties compared to coauthor-ship in quantitative work (Endersby 1996; Fisheret al. 1998). For social scientists, this suggeststhat work that is difficult to divide, such asethnography, will be coauthored less often(Babchuk et al. 1999).

DDAATTAA AANNDD MMEETTHHOODDSS

My primary interest is to identify the observedstructure of the social science collaboration net-work to distinguish between the three modelssuggested by commentaries on sociologicalpractice. Because participation is a necessaryminimum requirement for influence in the net-work, I first model participation in the network,and then examine the structure of the networkamong those who have coauthored.

SSAAMMPPLLEE AANNDD SSOOUURRCCEE

To examine network participation and embed-dedness, I use all English journal articles list-ed in Sociological Abstracts that were published

between 1963 and 1999. Nineteen sixty three isthe earliest date listed in the database, and 1999was chosen to ensure complete coverage with-in years. The Sociological Abstracts databasecovers all journals in sociology proper (all ASAjournals, for example), and many journals pub-lishing sociologically relevant work in otherfields (such as anthropology, political scienceand economics), and coverage has followed thegrowth in social science over the last 36 years.8

Sociological Abstracts limits coverage to jour-nal articles, neglecting conference presenta-tions, book reviews, essays, or books. Whilethese types of collaboration represent socialcontact, each has only spotty coverage in thedatabase. The exclusion of books is perhapsmost troubling, to the degree that books aremore common in particular specialties such associal movements or theory. While unfortunate,the generally lower rate of coauthorship in books



Figure 1. Coauthorship Trends in Sociology

8 There is no published universe of journals tocompare with, so it is impossible to know definitivelyif change in the number of journals reflects growthin the discipline or changes in database inclusion. SAprovides a coverage indicator, however, that tellshow often articles from a particular journal areindexed. I use this indicator in the models below tohelp account for possible inclusion differences.

may offset some of the error introduced by theirexclusion.

Authors are identified by name, which canlead to problems when names are inconsistentover time. Errors usually occur due to incon-sistent use of middle initials or when two peo-ple have the same name. Based on the observeddistribution of names, first and last names werecoded as either common or uncommon.9 If tworecords differed only in their middle initialsand had either the same uncommon last nameor the same uncommon first name (Howard (acommon name) Aldrich (an uncommon name)and Howard E. Aldrich, for example), they werecoded as being the same person. Second, thecoauthorship pattern was used to identify paperswhere the same author might use slightly dif-ferent names. For example, I assumed that twopapers written by “David Jacobs” and “DavidR. Jacobs”, with identical sets of coauthors,were written by the same person and, for thatpaper, “David Jacobs” is recoded to “David R.Jacobs.”10

MMEEAASSUURREESS

Actor information available from SociologicalAbstracts is somewhat limited, but we can getthe total number of unique publications, thenumber of unique coauthors, cohort, and timein the discipline (date of last publication minusdate of first publication). Publication volumeaccounts for productivity and increased oppor-tunities to coauthor. If the network structurewere random, embeddedness within the coreof the network would be determined entirely bynumber of collaborators. Due to changing trendsover time, those who enter the network latershould be more likely to coauthor than those

entering earlier. Star production models suggestthat those who have been in the discipline longershould be more deeply embedded than thosewho just entered. Finally, by linking first namesto the Census’gender distribution of first names,we can estimate the effects of gender on col-laboration and position in the network.11

Every article in Sociological Abstracts isassigned to one of 149 detailed subject codes,which are nested within 36 broad specialtyareas. These 36 areas are used to captureresearch specialties.12 Sociological Abstractsalso lists the number of tables in every article,which provides a simple proxy for whether thepaper uses quantitative methods.13 To control forchanges in journals covered by SociologicalAbstracts, I include an indicator for how com-pletely Sociological Abstracts indexes the jour-nals where people publish. Coverage is indicatedat three levels: complete (100% of the articlesin that journal are indexed), priority (more than50% of the articles are indexed), and selective(less than 50% are indexed).

I construct the collaboration network byassigning an edge between any two people whowrote a paper together, regardless of the howoften they have coauthored. Figure 2 demon-



09 A cutoff of 15 appearances of a name was usedto distinguish common from uncommon.

10 Prior work on large collaboration networks hasnot attempted to identify these types of errors. Analternative source for name cleaning would be to useauthor aff iliation. Unfortunately, SociologicalAbstracts, only lists affiliation for the first author, andauthors may have multiple affiliations (such as aresearch center and an academic department). Whilesuch corrections are important to help ensure accu-rate measures, the general graph features examinedhere do not differ significantly if I use the correctedversus the non-corrected data.

11 This results in a probability score based on theproportion of people with a given first name who aremale. Since not all first names can be matched, usingthis measure results in missing data for 32% of cases,likely predominantly among rare and foreign names.The substantive model results for the other variablesdo not differ with the inclusion of the gender meas-ure. Tables not using gender are available from theauthor upon request.

12 The sociological abstract area categories arelikely not substantively ideal, being subject to botherrors of misclassification and internal heterogene-ity. However, they remain the only tractable infor-mation on substantive area. While each recordcontains keywords describing content, the sheer num-ber of such words (over 8000 unique keywords)would require some sort of categorization routine, thedevelopment of which is not transparent.

13 This is also an imperfect measure, since whileall quantitative papers include tables some non-quan-titative papers also include textual tables, so thismeasure over-estimates the number of quantitativepapers. At the aggregate level used here, the smallnumber of nonquantitative papers with tables wash-es out relative to differences across specialties.

strates how the networks are constructed fromthe authorship data.14

The top panel of figure 2 is a schematic rep-resentation of data as given in SociologicalAbstracts, with authors (squares) connected tothe papers (circles) they write. The data includesingle authored papers (persons A,B,C and D)as well as those with more authors. The struc-ture on the top right of figure 2 represents a largeconnected set of authors, each of whom hascoauthored with someone who has coauthoredwith someone else. The bottom panel of figure2 provides the resulting collaboration network.Those who have written only single authoredpapers do not participate in the collaborationnetwork, but can be represented as structural iso-lates. Pairs of people who have only coauthoredwith each other are represented as isolated dyads{EF, GH}.

The largest connected component is the max-imal set of people who are connected by a chainof any length to each other. The large structureat the bottom right of figure 2 is the largestconnected component. Nested within this com-ponent is a bicomponent (circled). While a com-ponent requires only a single traceable pathbetween each actor, a bicomponent requiresthat there be at least two node-independentpaths connecting every pair of actors in the net-work. Simmel (1950) argued that the necessarycondition for a group is that a supra-individualbody remains even if a person leaves.Bicomponents meet this criterion, since thegroup remains connected even if a single per-son is deleted (Moody and White 2003). Thisconception scales, as tricomponents (3-nodeindependent paths), 4-components, and higherorder k-components identify increasingly cohe-sive subgroups in a network.

The degree distribution of the network isused to test the preferential attachment model.



Figure 2. Constructing Collaboration Networks

14 Thanks to a reviewer for suggesting this figure.

An actor’s degree is the number of unique peo-ple they are directly connected to, in this con-text the number of unique collaborators. Thedegree distribution for the example network isgiven in the lower right of figure 2. Geodesicpaths define network distance, as the number ofintermediaries on the shortest path connectedtwo nodes in a network. So, for example, nodesL and S are 3 steps apart.

PPUUBBLLIICCAATTIIOONN TTRREENNDDSS

The primary constraints on the shape of a col-laboration network are the distributions of thenumber of papers people publish and the num-ber of authors on a paper. Table 1 below givesthese distributions for all papers in the dataset(including those with only a single author).

Of all authors that appear in SociologicalAbstracts, 66% appear only once, and an addi-tional 15% appear only twice, with the numberof publications dropping quickly after that.Publication volume has increased slightly overtime. The percent of authors with only one pub-lication has dropped, from 71% in the 75–85period to 67% in the 89–99 period and the tailof the distribution is a little fatter.15 About 67%

of papers have 1 author, and 22% (66% of allcoauthored papers) have only 2 authors.Coauthorship increases over time, both ininstance (31% in the early period compared to38% in the later period) and extent (the averagenumber of authors per coauthored paper was2.40 in the early period, compared to 2.70 in thelate period). Even with the increase over time,these levels are low compared to the physicalsciences, which range from an average of 2.2authors per paper in computer science to 8.9authors per paper in high-energy physics(Newman 2001). A low number of authors perpaper decreases the size of complete clustersformed through common authorship on a sin-gle paper.

SSPPEECCIIAALLTTYY AARREEAA AANNDD NNEETTWWOORRKKPPAARRTTIICCIIPPAATTIIOONN

Having ever coauthored a paper is a necessarycondition for being embedded in the larger col-laboration network. If the collaboration net-work shapes commitment to particular ways ofdoing science, then identifying systematic dif-ferences in who collaborates will identify keydifferences in those exposed to the information



Table 1. Sociology Publication Patterns: Distributions of Publications, Coauthorship and Number ofCollaborators

Publications per Author Authors per Paper Unique Collaboratorsa

Count Total 1975–1985 1989–1999 Total 1975–1985 1989–1999 Total 1975–1985 1989–1999

00 — — — — — — 35.27% 41.06% 32.35%01 65.80% 70.57% 67.71% 66.83% 68.85% 62.57% 23.88 27.55 22.9902 15.05 14.80 15.59 21.88 22.78 22.79 14.48 14.53 15.0103 6.46 5.88 6.40 7.06 6.20 8.47 8.73 7.40 9.7304 3.63 3.05 3.44 2.49 1.73 3.41 5.34 3.74 6.1905 2.18 1.74 2.05 .93 .45 1.45 3.53 2.18 4.0406 1.50 1.21 1.34 .42 .17 .66 2.22 1.14 2.6707 1.08 .77 .87 .19 .07 .31 1.54 .67 1.7708 .82 .54 .63 .09 .03 .14 1.11 .45 1.3009 .60 .38 .46 .05 .01 .08 .81 .36 .9210 .46 .24 .33 .02 .01 .04 .59 .24 .6311 .37 .18 .26 .01 .01 .02 .48 .22 .47>11 2.04 .65 .80 .03 .01 .04 2.02 .46 1.93N 197,976 59,567 123,766 281,090 68,934 141,497 197,976 59,567 123,766

a Only people with coauthorships.

15 Note that the period-specific distributions onlycount publications within that period. Because theperiods do not cover all dates and individuals can pub-

lish in both periods, the three columns do not nec-essarily sum.

and ideas flowing through the collaborationnetwork.

Table 2 lists coauthorship level by broad spe-cialty area, the number of papers within eachcategory, growth in both number of articles and

coauthorship over time, and specialties sortedby the coauthorship rate.

While about 33% of all papers have beencoauthored, the range is high, from a low of8% for Marxist Sociology to 53% for Social



Table 2. Growth in Number of Articles and Coauthorship Rates, by Specialty, 1963–1999

Paper Coauthored Areas of Sociology N Papers (%) Growth (%) Coauthorship

GrowthAll Areas 281,090 100 346.68 33.2 .013Individual Areas

—Marxist 1,044 .37 1.66 8.0 –.009a

—Radical 908 .32 2.82 8.0 –.001c

—Knowledge 3,406 1.21 1.91 8.2 .000c

—History & Theory 17,231 6.13 17.03 12.9 .001c

—Culture and Society 7,040 2.50 3.67 14.3 .003

—Visual 141 .05 .25c 14.8 –.009b,c

—Language and Arts 5,673 2.02 7.44 17.8 .008—Political 14,412 5.13 15.93 18.1 .002—Science 4,518 1.61 5.28 20.0 .007

—Change & Economic Development 6,632 2.36 7.12 20.5 .007a

—Religion 5,569 1.98 4.84 23.6 .009a

—Group Interactions 8,611 3.06 13.48 24.4 .006

—Urban 4,444 1.58 .06c 25.6 .004

—Community Development 1,694 .60 –.39c,d 26.0 .009

—Feminist Gender Studies 7,225 2.57 11.9 27.2 .003a,c

—Social Development 9,805 3.49 15.93 27.9 .016—Social Control 7,804 2.78 6.16 28.4 .009—Policy & Planning 3,243 1.15 6.75 28.6 .008

—Clinical 280 .10 –.15c 29.8 .000b,c

—Mass Phenomena 12,069 4.29 14.98 30.0 .006

—Rural 3,746 1.33 .65c,d 30.5 .008—Education 10,628 3.78 8.71 31.9 .010—Environmental Interactions 3,102 1.10 7.93 32.1 .012—Methodology 8,897 3.16 6.68 32.1 .009—Studies in Violence 1,521 .54 3.11 33.4 .010—Demography 6,542 2.33 4.38 33.6 .010

—Social Differentiation 9,769 3.48 –.58c 34.4 .011—Studies in Poverty 1,393 .50 2.06 34.9 .014—Social Planning/Policy 12,232 4.35 20.21 35.8 .014—Complex Organizations 13,986 4.98 20.22 37.4 .012

—Business 195 .07 1.79 40.0 .054b,c

—Social Psychology 13,527 4.81 4.67 44.2 .013—Problems & Welfare 10,674 3.80 16.35 45.4 .019—The Family 19,806 7.05 20.13 46.1 .019—Health/Medicine 14,634 5.20 23.16 49.5 .025—Social Welfare 28,689 10.21 90.58 53.2 .045

a Trend levels off in recent years.b Recent category, less than 10 years covered.c Trend is not statistically significant.d Although the overall trend is not significant, growth is nonlinear, dropping sharply in the 1970s, and risingsteadily since about 1980.

Welfare.16 The number of articles in the data-base has increased by about 350 papers per yearover the past 30 years (column 4, row 1), andoverall growth by specialty can be seen in therest of the table (the sum of the specialty cellsequals the total). Culture and theory papers areamong the least likely to be coauthored, as arepapers in the sociology of knowledge, sociolo-gy of science, language and arts, radical soci-ology, Marxist sociology, political sociology,and visual sociology. Papers on social welfareare the most likely to be coauthored, followedclosely by those in social psychology, the fam-ily, sociology of health and medicine, and socialproblems and welfare. Work in these areas ofteninvolves large datasets and cumbersome analy-

ses that lend themselves to a substantive divi-sion of labor. There is a moderate correlationbetween growth in a specialty and the propor-tion of papers that are coauthored, though fieldssuch as social psychology and social theory /historical sociology clearly buck the trend. Theproportion of papers that are coauthored hasincreased by about 1% per year over the timespan covered in the database, and this trend hasbeen largely linear. Growth in coauthorship hasbeen relatively steady within specialties as well,with most having growth levels close to theaverage.

While the Sociological Abstracts data are toolimited to test many of the proposed explana-tions for increases in coauthorship, we can iden-tify the contribution of research method andspecialty area.17 Figure 3 plots the proportionof papers within a specialty area that are coau-thored against the proportion that have tables,for two time periods.

The f igure shows a strong correlationbetween the proportion of papers with tables in



Figure 3. Coauthorship and Quantitative Work 1975–1999 by Specialty.

16 Social Welfare is a large category includingtopics such as AIDS, health care, addiction, adoles-cence, illness and health care, marital and familyproblems, crime & public safety, etc. This categoryhas the highest coauthorship rate and is among thefastest growing areas in the dataset. To ensure that theobserved results were not simply an artifact of thiscategory, I have replicated the main findings for thepaper on a dataset that excludes these papers from theset. There are no substantively meaningful differ-ences when these papers are excluded.

17 Previous versions of this paper included data ona limited set of journals and examined funding, men-torship and location explanations for coauthorship.These tables are available on request.

each specialty and the proportion of papers thatare coauthored.18 The returns to quantitativework increased between the two periods, sug-gesting a stronger effect of quantitative work oncoauthorship in the most recent decade. A sim-ple explanation for the change over time is thatas quantitative work becomes more sophisti-cated, methodological specialists are broughtinto more projects. The increase in specializedknowledge needed for advanced techniquesincreases the value to dividing labor in papers(Hudson 1996).

Table 3 provides an individual level model ofhaving ever coauthored. Substantive area iscoded from the Sociological Abstracts as a countof the number of publications for each authorin each specialty area. Within each of the threetime windows, Model 1 presents a baselinemodel containing only the actor history, demo-graphic, publication and journal coverage vari-ables (the first 6 rows of the table) and a singlespecialty area. The specialty area coefficients inmodel 1 are thus the coefficients for separatemodels with the single specialty area and thefirst 6 variables listed in the table. We can thusinterpret the odds ratio as the change in theodds of coauthorship for each publication inthat specialty, relative to all other specialtyareas. Since people can write in multiple areas,and we would expect some areas to be muchmore closely related than others (Crane andSmall 1992; Daipha 2001), model 2 presents amultivariate version of model 1, entering all ofthe specialty areas simultaneously.19 Model 3adds an indicator of quantitative work, the pro-

portion of an author’s papers that have tables, tomodel 2, allowing us to identify specialty dif-ference net of research method.

The models show that those with greater timein the discipline (exposure) are slightly morelikely to coauthor, though the magnitude of thiseffect is small.20 The cohort effects are consis-tent, with those publishing later having a high-er likelihood of having coauthored, though againthe effect is relatively small. The strongest pub-lication effect is the simple number of publica-tions. Each additional publication increases theodds of coauthoring by 1.28. Consistent withprior research in economics, the odds of mencoauthoring are about .64 times the odds ofwomen coauthoring, though this effect decreas-es (to .73) once you control for specialty area.21

The actor-attribute effects remain largelyconstant when controlling for specialty or exam-ined over time. The clearest exception is that theeffect of the number of publications increaseswhen controlling for specialty area, and isstronger in the latter period than in the early peri-od. Similarly, cohort effects are less pronouncedin the later period than in the early period, like-ly reflecting the greater ubiquity of coauthorshipover time.

There is a clear effect of specialty on thelikelihood of having coauthored. Authors whowrite in historical, qualitative, radical and inter-pretive specialties are less likely to coauthorthan those writing in more positivist and quan-titative specialties. For example, the odds ofcoauthorship controlling for specialty overlap inMarxist sociology are about half (.57) those insociology of education, and those writing insocial history and theory are about .58 times aslikely. In contrast, those writing on the family



average in both time periods, the rate of change incoauthorship mirrors the rate of change overall, andthe area is large enough to provide a stable referencecategory.

20 To avoid redundancy in the text, I will focuscomments mainly on the pooled 1963–1999 models,and mention the other models only to the extent thatthe patterns differ from this model.

21 In addition, the control variables for SociologicalAbstracts coverage are also significant, showing thatjournals included only incidentally (the omitted cat-egory) are slightly more likely to be coauthored,though this effect either becomes insignificant orchanges direction when substantive area is included.

18 The correlation in the early period is .82, in thelate period it is .89. The change in slope between thetwo periods is statistically significant at p = .017. Toavoid clutter in the figure, each specialty is onlylabeled for one time period. An alternative to usingthe specialty areas would be to aggregate withinjournals, because with the exception of a few gener-al journals, journals specialize in particular topics.This figure is available on request, and substantiveresults are the same.

19 Since the categories are exhaustive, at least onemust be omitted. There is no substantive reason topick one area over another as the reference catego-ry, but a consistent reference category aids in evalu-ating change over time. In all models I used thesociology of education as the reference category,because the odds of coauthorship were very close to



Table 3. Logistic Regression of Having Ever Coauthored on Publication Characteristics

1963–1999 1975–1985 1989–1999

Variable Mod 1 Mod 2 Mod 3 Mod 1 Mod 2 Mod 3 Mod 1 Mod 2 Mod 3

Exposure 1.02 1.01 1.01 1.03 1.01a 1.02a 1.02 .99a 1.00a

Number of Publications 1.28 1.45 1.44 1.33 1.31 1.29 1.34 1.61 1.56Year of 1st Publication 1.03 1.02 1.01 1.04 1.03 1.01a 1.01 1.01 1.01a

Male Author (probability) .64 .73 .73 .70 .74 .76 .64 .76 .75Complete Coverage .92 1.22 1.19 1.16 1.32 1.22 .66 1.02a 1.03a

Priority Coverage .87 1.00a .94a .98a 1.01a .94a .80 .98a .91a

Quantitative Work .— .— 5.45 .— .— 4.17 .— .— 6.38Specialty Area of Sociology (code)—Radical (25) .26 .48 .61 .35 .58 .70a .20 .34 .47—Marxist (30) .32 .57 .62 .34 .50 .66 .20 .35 .49—Knowledge (22) .24 .38 .42 .26 .37 .48 .16 .26 .40—History & Theory (2) .49 .58 .64 .54 .68 .81 .35 .42 .54—Culture & Society (5) .52 .56 .62 .42 .49 .52 .37 .44 .57—Visual (33) .47 .57 .69a .44a .59a .86a .32 .35 .48a

—Language & Arts (13) .56 .59 .60 .51 .61 .69 .57 .59 .63—Political (9) .58 .68 .69 .60 .75 .74 .48 .59 .63—Science (17) .64 .69 .73 .67 .81 .92a .58 .64 .75—Social Change (7) .63 .77 .76 .65 .87a .93a .61 .75 .75—Religion (15) .74 .72 .72 .77 .85 .92a .76 .73 .72—Group Interaction (4) .64 .68 .71 .79 .90a .89a .58 .62 .66—Urban (12) .89 .91a .89 1.06a 1.20 1.14a .74 .78 .73—Community Development (23) .85 .90a .94a .85a 1.02a 1.16a .91a 1.04a 1.14a

—Female Gender (29) .68 .69 .71a .93a 1.01a 1.04a .55 .58 .66—Social Development (83=36) .82 .86 .76 .76 .87 .80 .78 .86 .79—Social Control (16) .88 .82 .84 .96a 1.02a 1.07a .87 .81 .86—Policy & Plan (24) .81 .87 .95a .79 .91a .96a .74 .79 .91a

—Clinical (31) 1.34 1.24a 1.21a .93a 1.01a 1.09a .76a .83a .98a

—Mass Phenomena (8) .90 .97a .95a .98a 1.13a 1.08a .82 .87 .88a

—Rural (11) .99a 1.07a 1.01a .86a 1.02a .96a 1.07a 1.08a .98a

—Education (14) .95 .— .— .89 .— .— .93 .— .——Methodology (1) 1.07 1.15 1.10 1.00a 1.13a 1.12a 1.17 1.25 1.22—Environmental (26) .95a 1.03a 1.02a .86a .97a .95a .96a 1.02a 1.02a

—Violence (28) .88 .89a .84 1.17a 1.24a 1.14a .73 .75 .75—Demography (18) 1.09 1.07a .93a 1.21 1.29 1.04a 1.03a 1.01a .83—Social Difference (10) 1.12 1.14 1.02a 1.17 1.28 1.13a 1.18 1.22 .97a

—Poverty (27) 1.07a 1.01a .94a 1.08a 1.30a 1.05a 1.12a 1.06a .96a

—Social Plan/Policy (72)/35 1.19 1.21 1.13 1.23 1.35 1.34 1.05a 1.10 1.13—Complex Organizations (6) 1.17 1.19 1.06a 1.25 1.38 1.23 1.17 1.20 1.03a

—Business (32) 1.39a 1.45a 1.26a .69a .81a .59a 1.76 1.99 1.80a

—Social Psychology (3) 1.88 1.91 1.63 2.21 2.34 1.95 1.60 1.77 1.50—Social Problems (21) 1.76 1.65 1.46 1.53 1.37 1.46 1.90 1.77 1.49—Family (19) 1.71 1.61 1.35 1.68 1.72 1.46 1.83 1.75 1.37—Health (20) 2.02 1.92 1.64 1.53 1.62 1.48 2.24 2.13 1.72—Social Welfare (61) 2.35 2.19 2.04 1.56 1.66 1.80 2.67 2.40 2.24R-Square .096 b .212 .322 .067 b .154 .254 .067 b .215 .352N 130,141 41,386 82,475

Note: Data shown as odds ratios. Unless otherwise noted, all cell values are significant at p ≤ .01 (tables withdetailed significance levels are available from the author). Mod = Model.a Value is not significant at the p < .01b Pertains only to the publication and demographic characteristics.

are 1.61 times more likely to coauthor, those insocial psychology are 1.91 times more likely tocoauthor and social welfare writers are 2.19times as likely to coauthor, net of individualattributes, index coverage, publication levelsand overlaps among area specialties. The impor-tance of specialty area for collaboration is clear-ly indicated in the increase in model fit. Thepseudo-R2 increases significantly with the addi-tion of specialty area.

The likelihood of coauthorship by specialtyarea does evidence some interesting changesover time. Many of the fields that are unlikelyto coauthor are comparatively more unlikely tocoauthor in the later period than in the early peri-od, suggesting that the importance of specialtyfor coauthorship has increased over time. Forexample, while those writing in feminist gen-der studies had about average coauthorship lev-els in the early period, the odds of coauthorshipin this specialty are about half the average in thelater period. Similarly, those writing in histor-ical sociology and theory had an odds ratio of.68 in the early period, compared to .42 in thelater period. The trend is replicated in reversefor fields where coauthorship is more common,though perhaps not as uniformly. For example,while methodologists had average levels ofcoauthorship in the early period (not statisticallydifferent from feminist gender studies), odds inthe later period are about 1.25 times that ofaverage groups. Similar patterns are evident forwork on the family. Social Psychology, on theother hand, is relatively more likely to coauthorin the early period than the later period, likelyreflecting its ‘first-mover’status in coauthorship.

Model 3 adds the indicator of quantitativework, and shows that quantitative work has astrong association with coauthorship. Overall,those writing quantitative papers are over 5times more likely to have coauthored than some-one who has not written quantitative work. Thiseffect has increased over time, from 4.17 in theearly period to 6.38 in the later period. Addingcoauthorship to the model signif icantlyimproves the model fit, attenuating the magni-tude of the specialty effects, though they aregenerally substantively similar to those in model2. This suggests that, while research method isclearly important, research specialty still makesa unique contribution to the odds of coauthor-ship.

These models suggest that coauthorshipmight be bifurcating across specialties, whichis likely due to an increasing rate of coauthor-ship growth in the less interpretive fields. Oncea division of labor process takes hold, it likelypropagates as mentors teach students that coau-thoring and collaboration is normative. As such,we should expect continual growth in the inci-dence of coauthorship within these specialtyareas. This bifurcation suggests that, to theextent that theoretical integration follows socialintegration, a theoretical gulf will mirror the net-work participation pattern. Those specialtiesthat are least likely to collaborate are thus lesslikely to be theoretically integrated with thosethat are more likely to collaborate.

CCOOLLLLAABBOORRAATTIIOONN NNEETTWWOORRKKSSTTRRUUCCTTUURREE

While the models presented above tell us whocoauthors, they tell us nothing about the struc-ture of the network given coauthorship. Thediscussion presented above suggest three com-peting models: A preferential attachment model,suggesting a structure reliant on star producers;a small-world model, where specialty areascluster into distinct social groups; and a struc-tural cohesion model that suggests a broad over-arching connectivity among a large portion ofthe network.

DDOOEESS TTHHEE NNEETTWWOORRKK DDEEPPEENNDD OONN SSTTAARR

CCOOLLLLAABBOORRAATTOORRSS??

If the observed network were generated througha preferential attachment process, the distribu-tion of number of coauthors would follow apower-law distribution, which will be seen as astraight line when plotted on a log-log scale. Thedistribution of the total number of collaboratorsis given in the last three columns of table 1, andpresented graphically in Figure 4. For the fullnetwork, about 37% of authors have only writ-ten with one other person, and about 22% havewritten with 2 others. The distribution of coau-thors has a fatter tail in the 89–99 period com-pared to the 75–85 period, with fewer nodeshaving only one coauthor and more people with3 or more coauthors.

The observed distribution does not fit a strictpower law, having a curved, rather than linearshape, suggesting that the network was not





Figure 4. Scale-Free properties of Coauthorship Networks

generated solely by a preferential attachmentprocess. Most prior research on power-law dis-tributions tests for a power-law by fitting aregression line to the observed points in thelog-log space. Using the simple regressionapproach, in each case the fit is improved byadding a squared term to the regression, indi-cating that the relation is not linear.22

Substantively, the key interest in scale-freenetworks lies in the extent to which a smallnumber of high-degree actors are responsible forconnecting the network, which puts them in auniquely powerful position for influencing thedirection of scientific practice. While the degreedistribution does not conform to a strict powerlaw, it is highly skewed, so it makes sense to testfor this structural property directly. The secondline in each panel of figure 3 (called componentsensitivity) gives the size of the largest con-nected component when all actors with thatdegree or more are removed from the graph. Itis clear from the figure, that the networks holdtogether well into the bulk of the tail, not dis-connecting until we remove all actors withgreater than 8–10 collaborators. While a minor-ity of the total network has 8 or more collabo-rators, one would still have to remove over 500of the most active nodes to disconnect the net-work. This network is not held together by asmall number of network stars. Thus, while thenetwork contains clear star actors – people witha disproportionate number of ties, and suchactors are likely very influential within a localregion of the network, information diffusionthrough the network does not depend on suchactors.

AA SSOOCCIIOOLLOOGGIICCAALL SSMMAALLLL WWOORRLLDD??

Is the social science collaboration network char-acterized by distinct clusters that are weaklyconnected to each other? Using formulas devel-oped by Newman et al. (2001), we can test theobserved graph properties relative to a randomgraph with a similar joint distribution of authorsand papers. Any network that has significantlygreater local clustering than expected by chanceand average distances about equal to chanceare considered small-world networks (Watts1999; Watts and Strogatz 1998). Formally, onemeasures local clustering with the clusteringcoefficient, C, which is the proportion of all two-step contacts (collaborator’s collaborators) thatare also directly connected (called the transi-tivity index in prior work [Davis 1970; Davisand Leinhardt 1972; Harary et al. 1965; Hollandand Leinhardt 1971]) and distance with l, theaverage path length between connected nodes.A small-world network has clustering that ishigher than expected and average distancesroughly equivalent to that expected in a randomnetwork of similar size and distribution of num-ber of partners.

The top panel of Table 4 compares the clus-tering and path length statistics for the observednetworks to the random expectation. For thefirst period, the observed clustering coefficient,C, is .194, which is not substantively differentfrom the expected random value of .207. For thetotal network, the observed characteristic pathlength, l, is 9.81, which is significantly longerthan the expected 7.57. Thus, distances aregreater, and relations less clustered, than wouldbe expected in a random graph with similarcontribution structures, which means the graphdoes not have a small-world structure.

This result is largely replicated for the twoperiod-specific networks. In each case, the clus-tering coefficient is a little smaller than randomexpectations, but distances are significantlygreater than expected under random mixing, indirect contradiction to the small-world model.These findings suggest that the collaborationnetwork is not composed of distinct, separateclusters. Instead, permeable theoretical bound-aries likely result in a network that folds in onitself, connecting people at greater distancesfrom widely different specialties.



22 Jones and Handcock (2003) have recently cri-tiqued much of this work. The regression methodimproperly weights cases and fails to account forautocorrelation between points. Their alternativemaximum likelihood techniques also suggest thatthe observed data do not conform to a preferentialattachment process. When viewed in the aggregate,the degree distribution is best fit with a variant of anegative binomial model, when viewed in the shortrun (75–85, 89–99) none of the standard models fitthe observed distributions particularly well, sug-gesting that many alternative factors determine thenumber of collaborators. Thanks to Mark Handcockfor providing these models.

SSTTRRUUCCTTUURRAALL CCOOHHEESSIIOONN??

The final model based on commentaries of thediscipline suggests a broad-based structurallycohesive collaboration network. The minimumrequirement for cohesion is connectivity, andthus increases in the size of the largest con-nected component are a basic requirement forstructural cohesion. While a necessary sub-strate, a component can be quite fragile, sinceremoving a single person can disconnect thenetwork. A stronger criterion for cohesion is thesize of the largest bicomponent.23

We need a benchmark to meaningfully judgethe size of a component or bicomponent inempirical networks. I construct comparison net-

works by randomly assigning the observed setof authors to the observed set of papers (whichretains the observed publication volume distri-butions), then construct a random collabora-tion network from these randomizedauthorships. This is preferable to simply ran-domizing edges in the full network, since itmaintains the necessary clustering that resultsfrom multiple authors on a single paper.Moreover, an authorship randomizationapproach allows me to control other mixingfeatures, such as homophily on number of pub-lications or the distribution of authors acrossspecialties. If changes in the inclusion of a par-ticular specialty with more coauthorships in thedatabase were driving results, randomizing with-in specialty would effectively account for thisbias. It should also be noted that componentsand bicomponents in random graphs effective-ly form an upper bound on component size,since under random mixing the componentsquickly converge to cover the entire graph(Palmer 1985). As such, the meaningful com-



Table 4. Comparison of Observed Coauthorship Structure to Equivalent Random Networks

1963–1999 1975–1985 1989–1999

Nodes (n)a 128,151 35,109 87,731Small-world Parameters—Cluster Coefficient .194 .306 .266——(Random expected) (.207) (.312) (.302)—Average Path Lengthb 9.81 12.26 11.53——(Random expected) (7.57) (8.31) (8.24)Size of Largest Component—Observed 68,285 7,492 36,772—Random Paper Assignment 95,078 16,736 59,736——(SD) (169) (131) (145)——Ratio of Observed to Random .72 .45 .62—Random Paper + One Publication 78,753 15,378 49,061——(SD) (132) (90) (120)——Ratio of Observed to Random .87 .49 .75Size of Largest Bicomponent—Observed 29,462 2,034 15,281——Ratio of Bicomponent to Component .43 .27 .42—Random Paper Assignment 47,339 4,774 29,738——(SD) (166) (94) (186)——Ratio of Bicomponent to Component .50 .28 .50——Ratio of Observed to Random .62 .43 .51—Random Paper + One Publication 48,769 7,882 31,806——(SD) (153) (78) (123)——Ratio of Bicomponent to Component .62 .51 .65——Ratio of Observed to Random .60 .26 .48

a Excludes people without coauthors.b Applies only within the largest connected component.

23 I focus here on the size of the largest bicompo-nent, but smaller bicomponents do exist in the net-work. However, they are usually many orders ofmagnitude smaller than the largest component. Forexample, the second largest bicomponent in thepooled network has fewer than 50 nodes.

parison over time is how much closer to therandom value we get, conditional on the relevantmixing features of the network. These compar-isons are given in the bottom panel of table 4.

Nearly half of all collaborating authors(68,285) are members of a single connectedcomponent, meaning that it is possible to tracea path from each to the other through coau-thorship chains. This is about 72% of chancelevels, if one simply assigned all authors topapers at random. In all years, the next-largestcomponent is orders of magnitude smaller thanthe giant component. For the full network, near-ly 60% of people who have coauthored but arenot in the largest component are scattered acrosscomponents of 2 or 3 people.

Simple random assignment, however, ignoresthe fact that many authors are only representedbecause they have coauthored a single paper thatgenerates isolated dyads (cases such as {E andF} in figure 2). We can set the randomizationprocess to match this parameter, ensuring thatour simulated network contains as many nec-essarily isolated dyads as observed in the realgraph (Random Paper + One Pub condition).Doing so lowers the expected size of the giantcomponent. Compared to this more realisticsimulation, the observed giant component isabout 87% of the random expectation.24

Looking over time, we see that the proportionof the population in the largest component hassteadily increased relative to random expecta-tion. Based on the one-pub restriction, thelargest component in the early period was 49%of random expectation, rising to 75% of randomexpectation in the later period.

We find a similar story with respect to the sizeof the largest bicomponent. Bicomponents arenested within components, and 43% (29,462people) of the members of the largest compo-nent are also members of the largest bicompo-nent, or about 60% of the random expected

size. Again, the relative proportion has increasedover time, moving from 26% in the early peri-od to 48% in the later period (based on the one-pub randomization model).25

Theoretical consensus should be higheramong pairs of people embedded in higher-order k-components. While a complete cohesiveblocking (Moody and White 2003) of the totalcoauthorship network is impossible because ofits size, we can estimate the distribution of high-er-order connectivity for the entire graph basedon the connectivity distribution among a sam-ple of dyads. By definition, every pair in thelargest component (N = 68,285) has at least 1path connecting them, and every pair withinthe largest bicomponent (N = 29,462) has atleast two paths. Nested within this bicomponent,the largest tricomponent has approximately14,627 (CI = 14,372–15,375) members, thelargest 4-component has approximately 7,992(CI = 6972–8068), and approximately 5,203(CI = 4564–5667) are connected by 5 or moreindependent paths.26

Higher-order connectivity appears to haveincreased over time as well. The bicomponentfor the 1975–1985 period is small enough toallow a complete enumeration of all nested con-



24 Additional controls were checked, includingconditioning on mixing by number of authors beyondisolated dyads (equivalent to fixing the diagonal ofthe mixing matrix, while the ‘one-pub’ conditiononly fixes the 1,1 cell), and constraining mixingwithin areas, which accounts for changes in special-ty representation over time. Neither of these restric-tions have as strong an effect on the expected valuesas the one-pub restriction. Tables available on request.

25 Because bicomponents must be nested withincomponents, and because our observed componentis smaller than the random component, directly com-paring the size of the observed bicomponent to therandom graph somewhat underestimates the relativecohesion in the observed network. To account for thisunderestimate, I present the ratio of the size of thelargest bicomponent to the size of the largest com-ponent for both the observed and the random graph.These figures also show that cohesion has increasedover time, from .51 in the early period to .65 in thelater period.

26 Estimates are based on the number of node-independent paths connecting randomly sampledpairs of nodes. I then back-estimate the size of the k-component from the distribution of node-independ-ent paths. I estimate conf idence intervals bybootstrapping the resulting distribution, then usingdistribution means at 5% and 95%. Because this esti-mate is based on a sample, it is impossible to iden-tify the sets of nodes that comprise the higher-orderk-components. These should probably be taken ashigh-end estimates, since people can belong to dif-ferent k-components, though my informal explo-rations of these data suggest that this is unlikely atthese lower k-levels.

nectivity sets. Here we find that while there aremany 3 and 4 components in the network, theyare always very small (usually less than 10members). These small higher-order k-compo-nents are linked together within the largerbicomponent in a manner that suggests a ‘ridge-structure’, where each group is partially embed-ded with other groups (Friedkin 1998). This isan image of a loose federation of coauthorslinked within the wider cohesive set (though thecore in the early period is only a small fractionof the total), with no significant schisms sepa-rating the network.

The 1989–1999 period admits to a greaterfraction of the (much larger) bicomponentembedded in higher-order k-components.Approximately 5,023 (CI = 4,827–5,200) nodesare embedded in a 3-component, while 2,763(CI = 2,559–2,885) are in a 4 component and1,616 (CI = 1,368–1,703) are in components ofk > 4. From these estimates, it appears that asubstantial number of social scientists are deeplyembedded within a highly cohesive coauthor-ship core, and that the size of this core hasincreased over time.

How are these cohesive sets related to eachother in the network? The general shape of thenetwork can be best represented with a con-tour sociogram.27 In a traditional sociogram,points are arrayed spatially to minimize the dis-tance between connected points and maximizethe distance between disconnected points, aswith the largest component in figure 2. For verylarge networks, a point-and-line sociogram isuninformative, because nodes simply crowdeach other out, stacking on top of each other toreveal a largely uninformative cloud (just as ascatter plot of thousands of points often resultsin what appears to be an even spread over theentire space). However, we can use the bivari-ate distribution of points in this space to iden-tify concentrations of nodes in the network.Any region of the graph with a comparativelyhigh level of cohesion will have a larger num-ber of nodes crowded together in that region, andthus a higher probability density value.28

Figure 5 presents the contour plot for the largestbicomponent of the pooled network.

The general spherical distribution of nodes(there are no nodes outside the .21 contour)suggests that actors are spread relatively even-ly across the possible space, showing no majordivisions.29 Within this comparatively flat ter-rain, there are two more prominent hills (seenhere as those areas with a density of 2.74 andhigher), connected by a high-density ridge(within the 2.11 contour). A primary subgroupanalysis of the largest bicomponent reveals thatthe two peaks differ in the topics studied.30 Thehighest hill in the “South-East” section corre-sponds to people writing in general sociology,while the “North-Central” hill has a concentra-tion of people writing in applied health fields.

As is always the case with cluster analytictechniques, it is tempting to reify such clustersat the expense of the general topology. Friedkin(1998) notes that the cluster structure of a net-work can be compared to geographical topog-raphy, and describes “ridge structures” as“sequentially overlapping and densely occu-pied regions of the social space” (p.125). Thisridge-structure image seems appropriate for theinternal structure of the observed collaborationnetwork. There are areas of relative concentra-tion, but they overlap substantially. As sug-gested by previous work on the structure ofsociology specialties (Cappell and Guterbock1992; Daipha 2001; Ennis 1992; Richards1984), high levels of intergroup contact, weakinternal structure, and strong overall connec-tivity point toward a generalized cohesion with-in the sociology coauthorship core. This is astructure that should promote theoretical inte-gration, since ideas and information can poten-



27 To my knowledge, this is the first time networkshave been represented with this type of figure.

28 The concentration of points is often very uneven,resulting in very jagged contour plots. I have used anon-parametric kernal density estimation technique

to smooth the resulting surface, with a bandwidth of.8. Alternative figures with less smoothing are avail-able from the author on request.

29 Contrast the relatively even distribution of nodeshere with a large racially segregated network, suchas http://www.sociology.ohio-state.edu/jwm/racel.gif.

30 Prior versions of this paper included a detailedanalysis of the subgroup structure of the core bicom-ponent. The two peaks correspond to clusters uncov-ered in the detailed cohesive group analysis.Combined they contain only 15.7% of the nodes inthe graph. These results are available from the authoron request.

tially flow quite freely, seldom getting trappedon one side of a topological chasm.

SSPPEECCIIAALLTTYY AARREEAA AANNDD SSTTRRUUCCTTUURRAALL

EEMMBBEEDDDDEEDDNNEESSSS

If a particular type of social science dominatesthe coauthorship core, then this connected groupmight have greater influence in shaping questionsin the field (Crane 1972). If, instead, specialtiesare evenly distributed across the network, then wehave greater evidence for functional integration(Hargens 1975). We can model individualembeddedness within the coauthorship networkusing a modeling strategy parallel to that used fornetwork participation. The dependent variablenow is structural embeddedness (Moody and

White 2003) in the network: those in small iso-lated components are coded 1, those in the largestconnected component are coded 2, and those inthe largest bicomponent are coded 3. Theseresults appear in Table 5.

In addition to measures defined for the net-work participation models, I add additionalmeasures based on the local network patterns.I control for network volume measures, includ-ing the number of unique collaborators and theaverage number of authors per paper.Mentorship captures the preferential attachmenthypothesis directly by looking at the relativepublication frequency of coauthors. Mentorshipis measured as the number of publications forthe focal individual minus the number of publi-cations for his/her coauthor, averaged over all



Figure 5. Social Science Coauthorship Network Largest Bicomponent (n = 29,462)



Table 5. Ordered Logistic Regression of Network Embeddedness on Network and Publication Characteristics

1963–1999 1975–1985 1989–1999

Variable Mod 1 Mod 2 Mod 3 Mod 1 Mod 2 Mod 3 Mod 1 Mod 2 Mod 3

Exposure 1.01 1.01 1.01 1.02a 1.02a 1.02a 1.04 1.05 1.06Number of Publications 1.17 1.12 1.13 1.36 1.32 1.34 1.33 1.14 1.16Year of 1st Publication 1.01 1.01 1.01 1.02 1.03 1.02 1.02 1.02 1.02Male Author (probability) .81 .83 .83 .91 .93a .93a .82 .85 .85Authors per Paper .97 1.01a 1.00a .82 .82 .81 1.00a 1.03 1.02a

Unique Coauthors 1.90 1.81 1.80 1.62 1.63 1.62 1.69 1.61 1.59Mentorship .85 .85 .85 .74 .74 .75 .72 .73 .73Coauthor Diversity .78 .86 .88 1.02a 1.0a 1.09a .97a 1.08a 1.14Complete Coverage 1.41 1.44 1.46 2.94 2.55 2.49 1.17 1.31 1.35Priority Coverage 1.16 1.17 1.14 1.54 1.42 1.38 1.09 1.11 1.08Quantitative Work .— .— 1.58 .— .— 1.55 .— .— 1.83Specialty Area (code)—Radical (25) .70 .95a .99a .32 .43a .47a .49 .77a .88a

—Marxist (30) .57 .76 .77 .56 .62 .65 .55a .87a 1.04a

—Knowledge (22) 1.20 .86 .87a .75a .89a .90a .59 .94a 1.06a

—History & Theory (2) 1.05a 1.01a 1.02a .98a 1.05a 1.07a .84 1.06a 1.09a

—Culture & Society (5) .97a .80 .82 .54 .55 .62 .64 .81 .88a

—Visual (33) .94a 1.02a 1.11a 1.01a .97a 1.33a .72a 1.30a 1.49a

—Language & Arts (13) .87 .80 .80 .61 .65 .69 .78 .95a .95a

—Political Sociology (9) .88 .99a .98a .74 .81 .81 .80 1.05a 1.04a

—Science (17) .81 .89 .90 .89a .94a .94a .68 .87 .92a

—Social Change (7) 1.02a .97a .97a .84a .87a .88a .72 1.00a .97a

—Religion (15) .95 1.01a 1.00a .97a 1.02a 1.00a 1.00a 1.19 1.15—Group Interaction (4) .97a 1.07a 1.07a 1.14a 1.17a 1.12a .96a 1.21 1.21—Urban (12) .87 .96 .95a 1.05a 1.05a 1.05a .71 .95a .92a

—Community Development (23) .68 1.15a 1.17a 1.52 1.50 1.51 .65 .89a .88a

—Female Gender (29) .95a .96a .96a 1.15 1.12a 1.13a .78 .94a .95a

—Social Development (83 = 36) .74 .82 .81 .80 .80 .81 .73 .93a .91a

—Social Control (16) .95 1.11 1.11 1.03a 1.04a 1.04a 1.01a 1.19 1.20—Policy & Plan (24) .83 .91a .94a .94a .96a 1.02a .82 1.07a 1.11a

—Clinical (31) .57 1.15a 1.17a 1.13a 1.13a 1.12a .04 .02 .03a

—Mass Phenomena (8) .81 1.09 1.09 .80 .83 .82 .92 1.15 1.14—Rural (11) 1.07 1.04a 1.03a 1.53 1.57 1.53 .92a 1.20 1.18—Education (14) .92 .— .— .98a .— .— .81 .— .——Methodology (1) 1.05a 1.14 1.13 1.08a 1.12a 1.10a 1.19 1.48 1.45—Environmental (26) .92 1.03a 1.05a 1.41 1.41 1.43 .85 1.08a 1.06a

—Violence (28) .96a .98a .98a 1.15a 1.12a 1.10a .98a 1.14a 1.13a

—Demography (18) .98a 1.06a 1.04a 1.30 1.30 1.21 1.00a 1.20 1.15—Social Differ. (10) .88 1.14 1.12 1.13 1.15a 1.11a .96a 1.21 1.16—Poverty (27) .96a 1.03a 1.02a 1.08a 1.19a 1.15a 1.03a 1.26 1.25—Social Plan/Policy (72/35) 1.01a 1.10 1.10 .83 .86 .85 .95a 1.16 1.19—Complex Orgs (6) .73 1.11 1.09 1.06a 1.08a 1.07a .90 1.10 1.06a

—Business (32) 1.12a 1.29a 1.28a 1.51a 1.46a 1.21a .99a 1.38a 1.25a

—Social Psychology (3) .91 1.19 1.17 .96a .97a .94a 1.07 1.33 1.29—Social Problems (21) 1.20 1.23 1.21 1.23 1.25 1.24 1.13 1.31 1.28—Family (19) 1.12 1.18 1.16 1.07 1.07a 1.04a 1.18 1.40 1.35—Health (20) 1.13 1.19 1.18 1.12 1.13a 1.12a 1.21 1.43 1.40—Social Welfare (61/34) 1.08 1.13 1.14 .86 .88a .88a 1.16 1.35 1.35R-Square .499b .506 .511 .404b .418 .422 .478b .489 .497N 86,498 24,897 56,632

Note: Data shown as odds ratios. Unless otherwise noted, all cell values are significant at p ≤ .01 (tables withdetailed significance levels are available from the author). Mod = Model.a Value is not significant at the p < .01b Pertains only to the publication and demographic characteristics.

coauthors. Mentors will thus have large positivescores. Coauthor diversity measures the extent towhich a person coauthors with different coau-thors, relative to their opportunity to coauthorwith others. This is calculated as the number ofobserved coauthors divided by the maximum pos-sible number of coauthors given the number ofpapers published and the number of authors oneach paper.31 We would expect that those whohave higher coauthor diversity would be moredeeply embedded in the coauthorship network.

As with network participation, time in the dis-cipline (exposure) increases the likelihood of beingin the core of the network, though more so in thelater period than in the early period. As expected,the number of publications is also a strong pre-dictor of being in the core, as is the number ofunique coauthors. Relative diversity of coauthor-ship patterns results in a lower likelihood of beingat the core overall, but the magnitude is small andinconsistent over time. In the later period, coau-thorship diversity increases the likelihood of beingat the core of the network, while it is nonsignifi-cant in the early period. Mentorship is consis-tently negatively related to being at the heart of thenetwork. To the extent that this type of publicationpattern captures shop production, it may be thatthose shops are relatively isolated or that studentsdo not then go on to write with new people.32 Justas males were less likely to coauthor, conditionalon coauthorship, males are less likely to occupythe core of the network suggesting that females arestrongly integrated into the overall network coreof the discipline.33

Looking at the multivariate models for spe-cialty area (model 2), the general pattern of coef-ficients is similar to that for network participation.Those writing in areas such as theory and culture

are slightly more likely to be at the periphery ofthe network and social psychology, sociology ofbusiness, family and social welfare more likely tobe at the core, but the magnitudes are much small-er. Many of the areas are not statistically distin-guishable from average, even with the largestatistical power in these models, and the relativesize of the odds ratios is much closer to 1 than inthe coauthorship models. A clear summary of theweakness of specialty for predicting embeddednessis seen in the change in model fit with and with-out specialty fields. Across all three networks, themodels only weakly improve by adding specialtyarea to the individual attributes. Turning to model3, we find that quantitative work increases embed-dedness, but has almost no effect on the value ofthe specialty area coefficients nor does it improvethe model much.

Substantively, these models suggest that onceone enters the coauthorship network, the key pre-dictors of position are individual and publicationcharacteristics. In contrast to network involve-ment, specialty area is a weak predictor of networkembeddedness. Turning this finding around, itsuggests that the cohesive core is spread relative-ly evenly across the specialty areas at risk to coau-thorship.34

CCOONNCCLLUUSSIIOONN AANNDD DDIISSCCUUSSSSIIOONN::SSOOCCIIAALL IINNTTEEGGRRAATTIIOONN IINN TTHHEE SSOOCCIIAALL SSCCIIEENNCCEESS

Coauthorship is becoming increasingly more com-mon in the social sciences. Nearly half of allpapers and over two thirds of all papers in the



31 Thanks to an anonymous reviewer for sug-gesting these measures.

32 I have also tested a cohort based mentor meas-ure, identifying those authors that are older thantheir coauthors (as measured by first publicationappearance). The measure is either unrelated tocore membership or negative, in much the sameway as the publication volume measure.

33 Prior analyses on the subgroup structure sug-gests that membership in smaller groups within thelargest bicomponent differ by gender, with malesmore likely to be in the peak region at the south-east corner of figure 5. As with the coauthorshipmodel, the embeddedness model benefits by con-

trolling for database coverage. Those authors whopublish in complete or priority journals are morelikely to be in the core of the network.

34 Previous readers suggested that this perme-ability might be due to the large number of peo-ple with few publications (such as graduatestudents who publish in disparate areas). Havingpublished little, they may be unimportant to thegeneral character of sociological production. Inresponse, I have constructed a network of peoplewith more than 3 publications and who have beenin the discipline for more than 5 years. This sig-nificantly lowers the sample size but not its gen-eral topology. A greater proportion of people arein the largest bicomponent, but the bicomponentis no more fractured than when using the fullsample.

ASR are coauthored. Coauthorship is not even-ly distributed across sociological work. As pre-dicted by others, coauthorship is more likely inspecialties that admit to an easier division oflabor. Research method seems particularlyimportant, showing that quantitative work ismore likely to be coauthored than non-quanti-tative work. While there is a specialty gap in net-work participation, among those who haveparticipated, specialty area is only a weak pre-dictor of network embeddedness. Thus, just asheterogeneity provides only limited informa-tion on social integration (Moody 2001), obser-vations about fractionalization in the disciplinebased on increasing numbers of specialtiesmight be misleading. The coauthorship patternshows a steadily growing cohesive core, sug-gesting that while authors might specialize,their skills marry well with others creating anintegrated collaboration network.

How do we account for the observed col-laboration pattern? Two complimentary imagesof science production are suggestive. Abbott’s(2001) description of social science as havingpermeable theoretical boundaries suggests thatspecialization within the social sciences doesnot necessarily generate divisions between spe-cialists. Instead, competitors actively borrowideas from each other (even if under newnames), to cover the available idea space. Thisfree mixing means that one’s coauthors neednot coauthor with each other, and thus the net-work as a whole admits to little clustering andfew schisms, instead spreading quickly over therelevant idea spaces represented in the disci-pline. Friedkin’s (1998) work suggests a spe-cialty analogue to Abbott’s competitive mixingmodel. Friedkin found that while contact clus-tered within specialties, these clusters wherestrongly connected to each other, creating net-work conduits through which ideas and infor-mation flow. Tie heterogeneity within groupsmeans that groups can act as bridges betweenother groups but still maintain internal cohe-sion (Paxton and Moody 2002). Fleshing thesehypotheses out will require examining theinternal structure of the collaboration networkin more detail, though preliminary work sug-gests that Friedkin’s model fits for short-runimages of later periods of the collaborationgraph.

What do these f indings suggest for theprospects of scientific consensus in sociolo-

gy?35 Data limitations demand cautious inter-pretations, but the structure is suggestive. First,the two most prominent models for consensusin an idea space are through references to rec-ognized authority (Martin 2002; Crane 1972) ordistributed interpersonal influence (Friedkin1998). Both models suggest that systematic dif-ferences in network participation will generatean ideational gulf between those involved inthe network and those without collaborations.In this case, the major divide centers largely onresearch method (quantitative or not) and the-oretical focus (radical, cultural, and interpretivemodes against largely positivist and empiricistmodes). While this gulf appears substantial,increasing collaboration over-time suggests thatit might be shrinking.

The two theoretical approaches offer slight-ly different predictions for future consensuswithin the connected collaboration network.The interpersonal influence model suggests thathigh overall cohesion will generate generalizedconsensus, as ideas circulate among the scien-tists connected in the network, though the long-term nature of the network suggests this mightbe a slow affair. A finer-level prediction is thatconsensus should be directly correlated withstructural embeddedness, and those embeddedin higher order k-components would be moresimilar to each other than those at the fringes ofthe network. However, the network does admitto a large inequality in numbers of collaborators,indicating clear stars even though these stars arenot essential for connecting the entire network.Martin’s authority-based perspective would sug-gest that those actors with many collaboratorsmight have much more influence shaping ideas



35 While suggestive, such proposals come withlarge caveats. Although coauthorship is a clear indi-cator of social connection, it is a stringent one. Thetrace of interaction can be found only after the col-laboration is recorded through publication. It is like-ly that other types of social interaction are layered ontop of the coauthorship network, which would like-ly lead to greater levels of cohesion than that observedthrough the coauthorship network. Second, theSociological Abstract area codes may correlate onlyweakly with the humanist-positivist division thattroubles many sociologists, building necessary ambi-guity into these findings. Third, the database ignoresbook publications, which might systematicallyexclude some areas more than others.

than others, perhaps acting as “pumps” for ideasthat are then quickly circulated through thewell-connected regions of the collaborationgraph.

While we lack the data necessary to answerthis question directly, I suspect that the twoapproaches are both correct for different aspectsof scientific consensus. I suspect that the highoverall cohesion levels will generate consensuswith respect to methods and rules of evidence,but that stars will act as “area authorities” withrespect to particular theoretical or empiricalclaims. Thus, as methodological change con-tinues to foster a division of labor based onhow we do research, this will generate consen-sus on what counts as valid evidence for mak-ing scientific claims. However, competition forstatus within the discipline will likely revolvearound stars who generate new ideas at theintersection of different research specialties.Classic treatments of the division of labor sug-gest that such integrated specialization shouldlead to organic solidarity, though this need notlead to a unif ication of particular ideas(Durkheim [1933] 1984; Hargens 1975;Hagstrom 1965; Whitley 2000). Perhaps, then,as Durkheim first suggested, cohesive collabo-ration networks will simultaneously allow fortheoretical diversity and scientific consensus.

James Moody is an Assistant Professor of Sociologyat the Ohio State University. His research focusesbroadly on social networks, with particular interestin linking individual action to global network dynam-ics. In addition to work on network methods, he hasworked on questions related to the formation anddynamics of adolescent friendship networks, the dif-fusion of sexually transmitted diseases, and the net-work foundations for social solidarity.

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The SStructure oof aa SSocial SScience Collaboration NNetwork: …jmoody77/asr_soccoauth.pdf · 2006-04-24 · social science collaboration network over time, and I link this structure