1 1 What are social networks? The set of (exchange) relationships between people or other social units. A directed graph, with people, groups, or organizations as the nodes and the things exchanged as the link Vary in size, density, clumpiness Some types of networks Communication Advice/information Friendship Trust/social support Tangible exchange/Material support Similarity Structure matters Clique Isolates Stars Boundary spanners 2 2 Why are they important? Examining social networks can help diagnose organizational problems Find informational bottlenecks/distribution channels Select successful team leaders and managers Good managers understand that there are both formal and informal networks in an organization Source of social capital, with benefits to individuals and organizations 3 3 Communication Flow in an R&D Lab (Where do you get technical info?) Allen, T. (1977). Managing the flow of technology. Cambridge, MA: MIT Press. 4 4 Race & school friendships Moody, Jame (2002) Race, School Integration, and Friendship Segregation in America. The American journal of sociology [ ] Moody yr:2002 vol:107 iss:3 pg:679 5 5 Familiarity in a CMU Project Class 79% non- Asian 83% Asian 6 6 Stunning Density Comparison Architecture BHA/BSA: 7 7 Bloggers X Party 8 8 Types of Relationships Among People 9 9 Why are they important? Actors who are connected, influence each other Goods (e.g., info, opportunities, power) flow through networks Actors position in the network influence their success Good managers/researchers cultivate extensive networks, inside & outside the organization at all levels 10 Social Capital Investment in time, energy and other resources in individual and organized social relationships Relationships have benefits Knowledge, innovation, resources Individual health and happiness Community efficiency, safety and quality 11 Social Capital One definition (Bourdieu, 1992: p. 119) The sum of the resources, actual or virtual, that accrue to an individual or a group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition 12 Social Capital 13 Social Support Health & Happiness Age-adjusted relative risk of dying among those lacking social contact during a 9-year period (Berkman, 1983) Sources of social support Being married Frequent contact with family and close friends Active member of a church Active participation in a club or other social group 14 Strength of ties Strong ties (Krackhard) Intimacy, self-disclosure, provide support Feel close w/frequent contact Spouse, relatives, close friends Weak ties (Granovetter) Diverse resources, broader base Feel distance w/infrequent contact Acquaintances, colleagues from elsewhere 15 Nature of the Social Tie Matters Strong tie = close relationship/friend Social relationship with high frequency, emotional commitment, multiplicity (overlap), and reciprocity Strong ties tend to know same things & people Strong ties tend to fill in the gaps (e.g., friends of friends become friends; friends tend to share taste) Strong ties useful for Money Advice Arduous help Friendship 16 Nature of the Social Tie Matters Weak tie = weak relationship/causal acquaintance Social relationships with low frequency, intensity, breadth, and reciprocity (Granovetter: Strength of Weak Ties) Hypothesis: Weak ties lead to more extensive and diverse social networks, and are more likely to overcome gaps of class, race, and other sources of division Data: Job changers get their jobs through weak ties: e.g.. only 16% from contacts they see weekly and 28% they see less than yearly Weak ties useful for New information Finding jobs 17 ~ 400M Facebook users either had a status update with a URL randomly deleted from their feed or not DV: Probability of friend posting the link URL retained: With link in feedURL deleted: No link in feed Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion Proceedings of the 21st international conference on World Wide Web. (pp ). Experiment on how info flows online 18 Feed subjects share more Subjects in feed condition were more likely to share Feed:.191% shared No feed:.025% shared Increase = 7.37x Shared fasted More exposure leads to more sharing 19 Strong are influential, but weak ties expose friends to new info Strong Ties Are More Influential per capita But effect of friend receiving the feed declines with tie strength stronger ties are getting the link from other sources 20 Weak ties were more influential than strong ties in the aggregate A single strong ties is more influential than a single weak one But people have many more weak ties than strong ones In aggregate, weak ties are more important 21 Basic Concepts Networks Tie Strength Key Players Cohesion CNM Social Media Module Giorgos Cheliotis u.sg) 21 How to represent various social networks How to identify strong/weak ties in the network How to identify key/central nodes in network Measures of overall network structure 22 Edge weights as relationship strength Edges can represent interactions, flows of information or goods, similarities/affiliations, or social relations Specifically for social relations, a proxy for the strength of a tie can be: (a)the frequency of interaction (communication) or the amount of flow (exchange) (b)reciprocity in interaction or flow (c)the type of interaction or flow between the two parties (e.g., intimate or not) (d)other attributes of the nodes or ties (e.g., kin relationships) (e)The structure of the nodes neighborhood (e.g. many mutual friends) Surveys and interviews allows us to establish the existence of mutual or one- sided strength/affection with greater certainty, but proxies above are also useful 22 23 Basic Concepts Networks Tie Strength Key Players Cohesion 23 How to represent various social networks How to identify strong/weak ties in the network How to identify key/central nodes in network Measures of overall network structure 24 Measures of Centrality 24 Degree Betweenness Closeness Eigenvector How many people can this person reach directly? How likely is this person to be the most direct route between two people in the network? How fast can this person reach everyone in the network? How well is this person connected to other well- connected people? Centrality measureInterpretation in social networks 25 Degree centrality A nodes (in-) or (out-)degree is the number of links that lead into or out of the node In an undirected graph they are of course identical Often used as measure of a nodes degree of connectedness and hence also influence and/or popularity Useful in assessing which nodes are central with respect to spreading information and influencing others in their immediate neighborhood Nodes 3 and 5 have the highest degree (4) Hypothetical graph 26 Betweenness centrality For a given node v, calculate the number of shortest paths between nodes i and j that pass through v, and divide by all shortest paths between nodes i and j Sum the above values for all node pairs i,j Sometimes normalized such that the highest value is 1or that the sum of all betweenness centralities in the network is 1 Shows which nodes are more likely to be in communication paths between other nodes Also useful in determining points where the network would break apart (think who would be cut off if nodes 3 or 5 would disappear) Node 5 has higher betweenness centrality than 3 27 Eigenvector centrality A nodes eigenvector centrality is proportional to the sum of the eigenvector centralities of all nodes directly connected to it In other words, a node with a high eigenvector centrality is connected to other nodes with high eigenvector centrality This is similar to how Google ranks web pages: links from highly linked-to pages count more Useful in determining who is connected to the most connected nodes CNM Social Media Module Giorgos Cheliotis u.sg) Node 3 has the highest eigenvector centrality, closely followed by 2 and 5 Note: The term eigenvector comes from mathematics (matrix algebra), but it is not necessary for understanding how to interpret this measure Values computed with the sna package in the R programming environment. Definitions of centrality measures may vary slightly in other software. 28 29 Basic Concepts Networks Tie Strength Key Players Cohesion 29 How to represent various social networks How to identify strong/weak ties in the network How to identify key/central nodes in network How to characterize a networks structure 30 Density A networks density is the ratio of the number of edges in the network over the total number of possible edges between all pairs of nodes (which is n(n-1)/2, where n is the number of vertices, for an undirected graph) In the example network to the right density=5/6=0.83 (i.e. it is a fairly dense network; opposite would be a sparse network) It is a common measure of how well connected a network is (in other words, how closely knit it is) a perfectly connected network is called a clique and has density=1 A directed graph will have half the density of its undirected equivalent, because there are twice as many possible edges, i.e. n(n-1) Density is useful in comparing networks against each other, or in doing the same for different regions within a single network density = 5/6 = 0.83 density = 5/12 = 0.42 Edge present in network Possible but not present 31 Dense Social Networks Are Good for the Group (Coleman, 1990) Dense networks are useful at the organizational level Provide Information & other resources Trust thru effects of reputation 32 Who Helps Whom with the Rice Harvest? Which Village Is More Likely to Survive? 33 Groups rely upon networks for success Allen: Bring technical knowledge into R&D teams Coleman: Rapid adoption of medical innovations among community of MDs Curtis: Software engineering Getting application domain requirements Keeping up with changing environment of use and development Ancona: New product development teams Convince the boss Get the support of "sister" departments 34 Allen: Gatekeepers Gatekeepers moderate the flow of technical information into R&D groups Connected both within and outside the group Technically competent & often a supervisor 35 Structural Holes Network closure relations are embedded in a network Enhance group identification Foster exchange of ideas Structural holes relations bridge disconnected networks Access to unique information Broker third parties 36 Structural holes benefits A structural hole exists when there is only a weak connection between two dense clusters Control benefits: brokers control the interaction between two network components Information benefits: brokers have access to unique information, this makes them invaluable Structural holes are a competitive advantage Separate non-redundant sources of information Information from different sources is more additive than overlapping 37 Advantages of Structural Holes (Burt, 2000)