1. Principles of Social Network Analysis

2. Definition of Social Networks “A social network is a set of actors that may have relationships with one another” (Hannemann, 2001) 2 /40

3. NODE: People Organizations Roles/positions TIE: Relationships Communications Resource-sharing Shared properties NETWORK: “Social entity” (group of friends, community, organization) 3 /40

4. Social Network Analysis Networks can have few or many actors (nodes), and one or more kinds of relations (edges) between pairs of actors.” “Social network analysis is the study of social structure and its effects.” 4 /40

5. Representation of Social Networks Matrices Graphs Nick Ann Rob Sue 5 /40

6. One way and or two way connections 6 /40

7. 7 /40

8. 8 /40

9. 9 /40

10. Clique: all the points have direct relationship 10 /40

11. 11 Clique: all the points have direct relationship 11 /40

12. 12 Bridge: a relationship which Connect two cliques 12 /40

13. 13 Isolate 13 /40

14. Structure matters … … for the flow of information / likelihood of collective action AB

15. Structure matters… …for the access that individual members have to information and opportunities Social capital and health: who you are connected to, and who they are connected to, has implications for your access to resources and your well-being 15 /40

16. Describing network structure Density = proportion of realized ties 16 /40 AB

17. Describing network structure Density = proportion of realized ties Density = 10/10 = 100% A Density = 2/10 = 20% B Indicator of the speed and completeness of information flow 17 /40

18. Clique: density equal to one 18 /40

19. Strength of weak ties C B D E F G A B C D E F G Weak ties (A—E) can be a source of strength 19 /40

20. Which one is a cluster? C B D E F G B C D E F G 20 /40

21. Clustering coefficient: likelihood that any 2 nodes that are connected to the same node are connected themselves. C B D E F G B C D E F G 21 /40

22. Clustering The degree to which decision making is done in collaborative groups. High rates of clustering are even more indicative of closed subgroups Clustering will inhibit spread between groups but accelerate it within groups Higher clustering will increase the importance of bridges that connect clusters 22 /40

23. Hierarchical structure 2 1 23 /40

24. Centralization 24 /40

25. Examples of Dense Networks (Density=36.4%) Decentralized (9.1%)Centralized (50.9%) 25 /40

26. Examples of Sparse Networks (Density=18.2%) Decentralized (0.0%)Centralized (87.3%) 26 /40

27. 27 27 /40

28. Centralization The centralization score is expressed as a percentage and can vary from 0 (every member is connected to every other member) to 100 (all members are connected to only 1 member). The centralization percentage thus indicates the degree of asymmetry in the distribution of connections in the network. A high centralization score indicates that some members have many more connections than others. 28 /40

29. Diffusion When Adopters Persuade Non-adopters at a Rate of One Percent TimeCumulativeNon-adoptersRate New Adopters 10.00100.000.01 25.0095.000.014.75 39.7590.250.018.80 418.5581.450.0115.11 533.6666.340.0122.33 655.9940.010.0124.64 780.6319.370.0115.62 896.253.750.013.61 999.860.140.010.14 10100.000.000.010.00 29 /40

30. Diffusion for Random Mixing 30 /40

31. Describing network structure Centrality “Betweenness” How often a node lies along the communication pathway between other nodes E B D C B E C D A A is a gatekeeper of information 31 /40

32. Describing network structure Centrality “Degree” Number of ties that a node has with others in the network Node-level indicator of influence and prominence within the network A sends out 3 ties and receives 2 E B C D A 32 /40

33. 33 2 1 33 /40

34. 34 2 1 34 /40

35. 35 2 1 35 /40

36. High indegree centralization would indicate that a small number of members are consulted by the rest of the members. High outdegree centralization would indicate that a small number of members do most of the consulting of others. 36 /40

37. 1. Degree Centrality: The number of direct connections a node has. What really matters is where those connections lead to and how they connect the otherwise unconnected. 2. Betweenness Centrality: A node with high betweenness has great influence over what flows in the network indicating important links and single point of failure. 3. Closeness Centrality: The measure of closeness of a node which are close to everyone else. The pattern of the direct and indirect ties allows the nodes any other node in the network more quickly than anyone else. They have the shortest paths to all others. We measure Social Network in terms of: 37 /40

38. Basic Network Concepts Density: proportion of pairs of points connected bylines Clique: all the points have direct relationship Component: Points that directly or indirectly connect to each other Bridge: a relationship which Connect two cliques Dyad: bilateral relationship between two points Degree: number of connections of each network element with others 38 /40

39. Basic Network Concepts Homophily: Tendency to form relationships with socially similar others Transitivity: Tendency toward closure that results in clustering within a network 39 /40

40. 12 8 9 7 6 5 4 3 10 12 11 2 Types of Network Data 1 2 Ego-CentricSociometric 40 /40