Analyzing the Evolution of Scientific Citations & Collaborations: A Multiplex Network Approach

Analyzing the Evolution of Scientific Citations &

Collaborations: A Multiplex Network

Approach By Soumajit Pramanik

Guide : Dr. Bivas Mitra

Citation Network

Important Author-based Metrics:• In-Citation Count• H-Index etc.

Co-Authorship Network

Previous works on Citation Network mainly focused on:

◦ Analyzing the evolution of citation and collaboration networks using “Preferential Attachment” [Barabasi et al. 2002]

◦ Understanding the importance of community structure in citation networks [Chin et al. 2006]

◦ Studying the evolution of research topics [He et al. 2009]

Existing Works

Previous works on Collaboration Network mainly focused on:

◦ Adopting social network measures of degree, closeness, betweenness and eigenvector centrality to explore individuals’ positions in a given co-authorship network [Liu et al. 2005].

◦ Analyzing the importance of the geographical proximity (same university/city/country etc.) of the collaborators [Divakarmurthy et al. 2011].

Continued…

1. Existing studies focused on the dominant factors like preferential attachment

2. None of these factors can be self- regulated.

3. Does their exist any self-tunable factor (suppressed by dominant factors) for boosting own citations/collaboration?

Motivation:

Continued…Advantage of attending Conferences:

Face-to-Face interactions with Fellow ScientistsStudying the influence of

such interactions on theevolution of Citation andCollaboration Networks

The authors, whose talks are scheduled in the same technical session of a conference, have high chances of interaction.

In general, the first or the last author (or sometimes both) of a paper attends the conference.

Assumptions:

Citations & Collaborations:

◦ DBLP Dataset for Computer Science domain (1960-2008)

◦ Around 1 million papers along with information about author, year, venue and references

◦ 501060 authors tagged with continents (using Microsoft Academic Search)

◦ 6559415 author-wise citation links

Real Dataset:

http://arnetminer.org/citationhttp://cse.iitkgp.ac.in/resgrp/cnerg/Files/resources.html

Interactions:

◦ Two domains: 1> Networking & Distributed Computing 2> Artificial Intelligence

◦ Selected 3 leading conferences from each domain:

1> INFOCOM, ICDCS, IPDPS from the first domain (1982-2007)

2> AAAI, ICRA, ICDE from the second domain (1980-2008)

◦ Collected session information from DBLP and program schedule of the conferences

Continued…

To regulate some important parameters and manifest their effects on the citation network

Followed statistics regarding articles per field per year, distribution of the number of authors in a paper and citation information from the real dataset

Only tunable parameter used: Successful interaction Rate p (p=0.1,0.2,…,1)

Synthetic Dataset:

Methodology: Multiplex Network Construction:

For each year t:

◦ Citation Layer: Directed author-wise citation links created at t, pointing to papers

published before t (or sometimes, in t)

◦ Interaction Layer: Undirected interaction links between authors presenting in same

sessions in selected conferences in t

◦ Co-authorship Layer: Undirected collaboration links between two authors if they co-author

a paper published in those chosen conferences in t

Continued…

1. Conversion Rate (CR) for a conference C for a

time-span T:

No. of “Successful” interactions in C during T

-------------------------------------- Total no. of interactions in C during T

From this, the definition of the Overall Conversion rate can be simply extended.

Evaluation Metrics:

2. Induced Citation Link Repetition (LR):

LR measures the no. of times each “induced” citation link appears within the recorded time period.

3. Lifespan of Induced citation (LS):

The Lifespan of an “induced” citation is measured as the difference between the first and the last appearing year of the “induced” citation link.

Continued…

4. Rate of appearance (RA):

The rate of appearance of the of a induced citation link is denoted by the ratio of the repetition count and lifespan. Hence RA = LR / LS

5. Influence of successful interaction (IG):

The influence of a “successful” interaction is measured as the latency between the “successful” interaction and the formation of the first induced citation.

Continued…

Interactions to Citations

Real Datasets:

Conversion Rates

Networking Domain:2.87% (381 out of 13240) for [0.9,0.1] interaction probabilities

AI Domain:2.1% (1291 out of 61896) for [0.9,0.1] interaction probabilities

Synthetic Dataset:

Continued…

Downfall near end years due to “Boundary Effect”

Heat-Maps

Networking Domain:

1. Overall Value increasing2. Distributed Contribution

AI Domain:

1. Overall Value slowly increasing2. Dominated Contribution

Induced Citation Repetition (LR) & Lifespan (Ls)

In both domains,

1. Power-Law distribution2. A significant no. of “induced” citations repeat a high no. of times

AI Domain

Networking Domain

Significant no. of “induced” citations have high RA values

Reasons can be a) Low LS or/and b) High LR

AI Domain

AI Domain

NetworkingDomain

Continued…AI Domain

Networking Domain

1. High RA ratio results from mainly low LS2. Ä large no. of induced" citations missing from the right side of the plot due to the boundary effect.

1. Aperiodicity of repetitions of “induced” citations increase almost linearly with their Lifespan2. High LR not necessarily imply high standard deviation AI Domain

Networking Domain

Influence Gap (IG)

Influence of Continents

1. All the highly repeating “induced” citations have low “Influence” Gap

Dominance of North America-North America pairs

AI Domain

AI Domain

Networking Domain

Networking Domain

Domain LR vs LS Standard Deviation

vs LS

LR vs IG LS vs IG

Artificial Intelligenc

e

0.57 0.98 -0.13 -0.12

Networking &

Distributed Systems

0.61 0.97 -0.14 -0.13

Correlation Values

Citations To Collaborations

Conversion Rates◦ 1. Considered only collaboration between established

researchers (having at least 1 publication)

◦ 2. In Networking domain out of 8920 co-author links, 2495 (28%) exhibits a past history of mutual citations!

◦ 3. In AI domain 3211 out of 10192 (31.5%) are such “induced” co-author links.

Induced Collaboration Repetition Count and Influence GapHere also, all highly repeating“induced” collaborations have small “influence” gap

AI Domain

Networking Domain

Component EvolutionNetworking Domain: 1. Giant component size 8152, Second Largest Component size 63

2. 28% (167) of induced collaboration links took part in the merging process

AI Domain: 1. Giant component size 16203, Second Largest Component size 41 2. 36:6% (263) of induced collaboration links took part in the merging process

Interactions during conferences can be used as a tool to boost own citation-count.

This can indirectly help in creating effective future collaborations and this cycle goes on.

With time people are being more and more aware about the benefits of interacting with fellow researchers during conferences.

Conclusion & Future Plans

Need to check

1. Influence of specific fields of interacting authors on creation of “induced” citations

2. Effects of “induced” citations/collaborations on the citation/collaboration degree distribution

3. Modeling the dynamics

1. A. L. Barabasi, H. Jeong, Z. Neda, E. Ravasz, A. Schubert, and T. Vicsek: “Evolution of the social network of scientic collaborations”. Physica A: Statistical Mechanics and its Applications, 311(3-4):590 - 614, 2002.

2. A. Chin and M. Chignell.: “A social hypertext model for finding community in blogs. In HYPERTEXT '06”. Proceedings of the seventeenth conference on Hypertext and hypermedia, pages 11-22, New York, NY, USA, 2006. ACM Press.

3. Q. He, B. Chen, J. Pei, B. Qiu, P. Mitra, and C. L. Giles: “Detecting topic evolution in scientific literature: how can citations help?” In CIKM, pages 957-966, 2009.

4. X. Liu, J. Bollen, M. L. Nelson, and H. Van de Sompel.: “Co-authorship networks in the digital library research community”. Information processing & management, 41(6):1462-1480, 2005.

5. P. Divakarmurthy, P. Biswas, and R. Menezes.: “A temporal analysis of geographical distances in computer science collaborations”. In SocialCom/PASSAT, pages 657-660. IEEE, 2011.

References

Thank you…