Social Network Analysis: What It Is, Why We Should Care, and What We Can Learn From It
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This document provides an overview of social network analysis, including what social networks are, what can be learned from analyzing social networks, and how social network analysis can be performed. Some key findings that can be uncovered include the six degrees of separation principle, the 80-20 rule of social popularity where a minority of nodes have most connections, how to identify influential nodes, and how to group similar nodes into communities. Various metrics and models are described for analyzing features like path lengths, degree distributions, ranking nodes, measuring community structure, and more. Examples of social network analysis are also provided.
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Social Network Analysis: What It Is, Why We Should Care, and What We Can Learn From It
- 1. Social Network Analysis What it is, what we can learn from it, and how we can do it
- 2. Backstrom & Kleinberg, 2014
- 3. Predict your partner 1.3m users in a relationship, 379m people, 8.6b connections
- 4. Predict your partner 1.3m users, 379m people, 8.6b connections Accuracy: 60%
- 5. Andris et al., PLoS ONE, 2015
- 6. What is it?
- 10. Social networks connect people
- 11. Social network A social structure that represents the relationships between people
- 12. Social network A graph model that represents the dyadic relationships between people
- 14. Nodes and Edges
- 15. Nodes and Edges
- 16. Nodes and Edges
- 17. Nodes and Edges
- 19. Network A graph model that represents the dyadic relationships between entities
- 21. Ahn et al.,Sci. Rep., 2011
- 22. Weighted network Links can have a weight
- 23. Directed network Links can be directed
- 24. Rosvall & Bergstrom,PNAS, 2009
- 25. Network = Graph
- 26. Network analysis = Graph theory
- 27. Network analysis = Graph theory+statistics+physics
- 28. What can we learn from it?
- 29. Six degrees of separation 80-20 rule of social popularity
- 30. Grouping: Group similar people together Ranking: Find the most influential people
- 31. Six degrees of separation
- 32. Six degrees of separation Any person in the world needs only to take at most six steps to reach any other person
- 33. How to measure how separated we are?
- 34. Diameter Longest shortest path among all pairs How many steps a person has to take to reach anyone
- 37. Average path length Shortest path lengths averaged among all possible pairs
- 44. Milgram’s experiment Omaha & Wichita -> Boston 64 out of 296 reached destination Average path length 5.5
- 45. Watt’s Experiments 60,000 email users to reach 18 targets in 13 countries Dodds et al., Science, 2003
- 47. Back-of-the-envelope calculation 50k = 7,000,000,000 k = log50 (7,000,000,000) = 5.8
- 48. Erdos number Mathematicians’ co-publishing network Average path length – 4.65
- 49. Bacon number Actors’ co-starring network Average path length – 3.65
- 51. 1
- 52. 2 1
- 53. (some mathematicians and papers that nobody cares about)
- 54. 5 2
- 56. Be nice to the old lady slowly crossing the street
- 57. 80-20 rule of social popularity
- 59. How to measure the popularity of the nodes?
- 60. Degree distribution The probability distribution of the number of links of a node P(deg(v)= k)
- 62. N=10000, p=0.02
- 64. What does a real network look like?
- 65. Mahadevan et al., SIGCOMM, 2007
- 66. Newman, SIAM Review, 2003
- 67. Power-law distribution P(k) ~ k-g
- 68. Theory vs Reality H. Jeong et. al, Nature (2000)
- 69. Power-law distribution Long-tail distribution: Nodes with extremely large number of links have non-trivial chance to appear
- 70. Power-law distribution 80-20 rule of social network: A minority of nodes have a majority of links
- 72. What’s wrong with the random network model?
- 73. Barabasi-Albert model When a new node joins the network, it connects to popular existing nodes
- 74. Barabasi-Albert model When a new node joins the network, it connects to popular existing nodes The probability is proportional to the existing node’s number of links
- 76. Scale-free network Matthew Effect: Richer-get-richer Links is the “wealth” in social network
- 77. Like wealth, social popularity follows 80-20 rule
- 78. Rank the nodes according to their important
- 79. PageRank Relative importance of nodes
- 80. PageRank Probability of opening a page when surfing the Internet
- 81. PageRank Probability of arriving at this page from other pages that link to it
- 82. PageRank Probability of arriving at this page from other pages that link to it, or randomly opening this page
- 86. Group similar nodes together
- 88. Communities Friends from middle school, college, and work How do we group them?
- 89. Guimera & Amaral, Nature, 2005
- 90. How to measure the tendency to form communities?
- 91. Modularity How well the network can be separated into modules
- 93. For every module, calculate the number of links within the module
- 94. For every module, calculate the number of links within the module, minus its expectation
- 95. Compare to its expectation
- 96. Maximize modularity NP hard Simulated annealing: slow, accurate Louvain method: greedy, fast, local- minimum
- 99. Guimera et al., PNAS, 2005
- 102. Dynamics on network SIR model in NetLogo
- 103. Multiplex network Facebook, Twitter, LinkedIn
- 104. Radicchi & Arenas, Nature Physics, 2014
- 105. De Domenico et al., PNAS, 2014
- 106. How do we do it?
- 108. Co-purchase network Who bought the same deals as you did?
- 109. Data User 1, Deal A User 1, Deal B User 1, Deal C User 2, Deal A User 3, Deal B …
- 110. 1 A 2 B 3 C 4 D
- 111. 1 A 2 B 3 C 4 D
- 112. 1 A 2 B 3 C 4 D
- 113. 1 2 3 4
- 114. 1 2 34
- 115. IPython notebook
- 117. Diameter: 6 Average path length: 2.57
- 119. Largest groups Group 1 - 395 females, 201 males - Average age: 42.4 Group 2 - 228 females, 83 males - Average age: 38.9 Group 3 - 51 females, 27 males - Average age: 34.1
- 120. Group 1 vs 2: p-value = 0.001 Group 1 vs 3: p-value < 0.001 Group 2 vs 3: p-value < 0.001
- 122. IPython notebook Examples of networks: https://github.com/zengxiaohanzxh/network s-ipython.git Groupon example: git@github.groupondev.com:zengxiaohanzx h/networks.git
- 123. Xiaohan Zeng <xzeng@groupon.com> Quantum Lead, 6th floor near Fishbowl
Editor's Notes
- Thank you for the intro, I’m Han, very glad to be part of this great tradition, share a few exciting things that I know about sna
- While you are working on the sandwich, let me share two examples that demonstrate the power of social network analysis
- This network is about people, but the relationship is their board membership. If you think money is their friendship
- Not only colorful but also very tasteful network
- This network is about papers
- What knowledge have we gained? Social network is a complex system where patterns only emerge on a large scale
- Powerful tools to understand the rich and complex structures
- We feel the world is small, your friend posts a message and another friend replies, your best friend from primary school is marrying your college roommate
- Inspired by the fact that people move around and form new connections In the first 17 years of my life…
- We already have theories, what do we need next? Experiments!
- We already have theories, what do we need next? Experiments!
- Bacon started a charitable organization, sixdegrees.org
- Bacon started a charitable organization, sixdegrees.org
- Me – my wife – Prof. Mirkin – President Obama
- This is where statistics come into play
- Erdos-Renyi network
- There is no such a number as scale
- Prob to connect is not the same
- NetLogo example
- NetLogo example
- NetLogo example
- NetLogo example
- A friend of mine, now a professor at IU
- So I took the inspiration and made a perhaps more exciting example
- No information on the airport itself
- Actually shot in Evanston, my friend saw Matt Damon. The world is indeed very small!
- We are not Facebook or LinkedIn, social network is not our product
- If you are interested in the general idea of network science, read Linked. If you are eager to see the math behind it, read Newman’s book
- Finally, I work at Quantum Lead, and we do data-driven sales optimization. We are located at the SW corner of 6th floor, near Fish Bowl. Come to talk to us if you are interested, we have a very talented team, awesome engineers and awesome managers. With that, I will end my talk today. Thanks very much for your attention and please let me know if you have any questions!