Social Network Analysis (Part 2)
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The document focuses on social network analysis, particularly different notions of centrality such as indegree, outdegree, betweenness, and closeness. It explores examples from real-world networks, including petroleum trade and financial trading networks, highlighting the significance of these centrality measures. Additionally, it discusses connected components and the concept of a giant component within a network.
![betweenness: definition
å
CB (i) = gjk (i) /gjk
j<k
Where gjk = the number of shortest paths connecting jk
gjk(i) = the number that actor i is on.
Usually normalized by:
CB
' (i) = CB (i ) /[(n -1)(n -2) /2]
number of pairs of vertices
excluding the vertex itself](https://image.slidesharecdn.com/snaslides2-141008213416-conversion-gate01/85/Social-Network-Analysis-Part-2-15-320.jpg)
Social Network Analysis (Part 2)
- 1. Social Network Analysis Dr. Vala Ali Rohani Vala@um.edu.my VRohani@gmail.com Part 2: Centrality
- 2. different notions of centrality In each of the following networks, X has higher centrality than Y according to a particular measure Y X Y X X Y Y X indegree outdegree betweenness closeness
- 3. review: indegree Y X
- 4. trade in petroleum and petroleum products, 1998, source: NBER-United Nations Trade Data
- 5. • Which countries have high indegree (import petroleum and petroleum products from many others) • Saudi Arabia • Japan • Iraq • USA • Venezuela Quiz Q:
- 6. review: outdegree Y X
- 8. • Which country has low outdegree but exports a significant quantity (thickness of the edges represents $$ value of export) of petroleum products • Saudi Arabia • Japan • Iraq • USA • Venezuela Quiz Q:
- 9. putting numbers to it Undirected degree, e.g. nodes with more friends are more central.
- 10. normalization divide degree by the max. possible, i.e. (N-1)
- 11. real-world examples example financial trading networks high in-centralization: one node buying from many others low in-centralization: buying is more evenly distributed
- 12. In what ways does degree fail to capture centrality in the following graphs? what does degree not capture?
- 13. Brokerage not captured by degree Y X
- 14. betweenness: capturing brokerage • intuition: how many pairs of individuals would have to go through you in order to reach one another in the minimum number of hops? X Y
- 15. betweenness: definition å CB (i) = gjk (i) /gjk j<k Where gjk = the number of shortest paths connecting jk gjk(i) = the number that actor i is on. Usually normalized by: CB ' (i) = CB (i ) /[(n -1)(n -2) /2] number of pairs of vertices excluding the vertex itself
- 16. betweenness on toy networks • non-normalized version:
- 17. betweenness on toy networks • non-normalized version: A B C D E A lies between no two other vertices B lies between A and 3 other vertices: C, D, and E C lies between 4 pairs of vertices (A,D),(A,E),(B,D),(B,E) note that there are no alternate paths for these pairs to take, so C gets full credit
- 18. betweenness on toy networks • non-normalized version:
- 19. betweenness on networks • non-normalized version: A B C E D why do C and D each have betweenness 1? They are both on shortest paths for pairs (A,E), and (B,E), and so must share credit: ½+½ = 1
- 20. Quiz Question • What is the betweenness of node E? E
- 21. betweenness: example Lada’s old Facebook network: nodes are sized by degree, and colored by betweenness.
- 22. Quiz Q: Find a node that has high betweenness but low degree
- 23. Quiz Q: Find a node that has low betweenness but high degree
- 24. closeness • What if it’s not so important to have many direct friends? • Or be “between” others • But one still wants to be in the “middle” of things, not too far from the center
- 25. need not be in a brokerage position Y X Y X X X Y Y
- 26. closeness: definition Closeness is based on the length of the average shortest path between a node and all other nodes in the network Closeness Centrality: N å ê ê -1 ú ú Cc (i) = d(i, j) j=1 é ë ù û Normalized Closeness Centrality ' (i) = (CC (i)) /(N -1) CC
- 27. closeness: toy example A B C D E ' (A) = Cc d(A, j) N å j=1 N -1 é ê ê ê ê ë -1 ù ú ú ú ú û = 1+ 2 +3+ 4 4 é ë ê -1 = ù û ú é 10 4 ë ê -1 = 0.4 ù û ú
- 29. Quiz Q: Which node has relatively high degree but low closeness?
- 31. Connected Components • Strongly connected components • Each node within the component can be reached from every other node in the component by following directed links Strongly connected components B C D E A G H F Weakly connected components: every node can be reached from every other node by following links in either direction A B C D E F G H A B C D E F G H Weakly connected components A B C D E G H F In undirected networks one talks simply about ‘connected components’
- 32. Giant component • if the largest component encompasses a significant fraction of the graph, it is called the giant component http://ccl.northwestern.edu/netlogo/models/index.cgi