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SOCIAL NETWORKS ANALYSIS SEMINAR INTRODUCTORY LECTURE Danny Hendler and Yehonatan Cohen Advanced Topics in on-line Social Networks Analysis.

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Presentation on theme: "SOCIAL NETWORKS ANALYSIS SEMINAR INTRODUCTORY LECTURE Danny Hendler and Yehonatan Cohen Advanced Topics in on-line Social Networks Analysis."— Presentation transcript:

1 SOCIAL NETWORKS ANALYSIS SEMINAR INTRODUCTORY LECTURE Danny Hendler and Yehonatan Cohen Advanced Topics in on-line Social Networks Analysis

2 Seminar requirements Select a paper and notify me by March, 17'th Study the paper well and prepare a good presentation Give an excellent seminar talk Participate in at least 80% of seminar talks

3 Seminar schedule Introductory lecture #1 5/3/14 No seminar (Purim!) Semester ends 12/3/14 Introductory lecture #2 Papers list published, students send their 3 preferences 14/3/14 11 weeks of Student talks 19/3/14 Student talks start All students preferences must be received 10/3/14 26/3/14

4 Talk outline Social networks Properties of on-line social networks Small-world phenomenon Power-law distribution Community structure Community detection

5 Social networks What is a social network? A network, were nodes represent actors and edges represent interactions/relationships

6 Social networks: an example

7 Giant component

8 Social networks: an example Some nodes are very active

9 Types of online social media

10 Top 20 USA websites 1Google.com11Blogger.com 2Facebook.com12msn.com 3Yahoo.com13Myspace.com 4YouTube.com14Go.com 5Amazon.com15Bing.com 6Wikipedia.org16AOL.com 7Craigslist.org17LinkedIn.com 8Twitter.com18CNN.com 9Ebay.com19Espn.go.com 10Live.com20Wordpress.com Source: Alexa report, February, 2014

11 Top 20 USA websites 1Google.com11Blogger.com 2Facebook.com12msn.com 3Yahoo.com13Myspace.com 4YouTube.com14Go.com 5Amazon.com15Bing.com 6Wikipedia.org16AOL.com 7Craigslist.org17LinkedIn.com 8Twitter.com18CNN.com 9Ebay.com19Espn.go.com 10Live.com20Wordpress.com 30% social network sites Source: Alexa report, February, 2014

12 Top 20 USA websites 1Google.com11Blogger.com 2Facebook.com12msn.com 3Yahoo.com13Myspace.com 4YouTube.com14Go.com 5Amazon.com15Bing.com 6Wikipedia.org16AOL.com 7Craigslist.org17LinkedIn.com 8Twitter.com18CNN.com 9Ebay.com19Espn.go.com 10Live.com20Wordpress.com 30% social network sites 30% additional sites with social network aspects Source: Alexa report, February, 2014

13 Social networks Properties of on-line social networks Small-world phenomenon Power-law distribution Community structure Community detection Properties of social networks

14 Milgram's small world phenomenon experiment (1967) Six degrees of separation:I read somewhere that everybody on this planet is separated by only six other people. Six degrees of separation between us and everyone else on this planet. (*) (*) John Guare. Six Degrees of Separation: A Play. Vintage Books, So, Milgram decided to check…

15 Milgram's experiment (cont'd) Budget: $680!!! A set of starters, all try to forward a letter to a single target person Starters notified of targets name/address/occupation Must forward letter to someone known on first-name basis Image taken from Wiki.

16 Milgram's experiment: results 64 chains arrived Median length: 6 Taken from: Networks, crowds and Markets, D. Easley & J. Kleinberg. (Book is online)

17 A slightly more modern example (2008)

18 Social networks Properties of on-line social networks Small-world phenomenon Power-law distribution Community structure Community detection Properties of social networks

19 A matter of popularity… As a function of k: what fraction of Web pages have k in-links? ~1/k 2.1 (*) (*) Broder et al. Graph structure in the Web. WWW 2000, pp

20 The power low distribution Degrees Nodes A.k.a. long tail distribution, scale-free distribution Most nodes have low degrees Few nodes have extremely high degrees

21 Web pages in-degree: log-log scale

22 Some more examples Friendship Network in FlickrFriendship Network in YouTube

23 Why is popularity power-law?

24 A simple game… Procedure for creating Web page j {1,2…N} Choose page i

25 Rich get richer… Procedure for creating Web page j {1,2…N} Choose page i

26 The situation in random graphs Nodes connected at random Node degrees follow a binomial distribution

27 number of nodes found Power-law graph: BFS Animation taken from a presentation by Ofrit Lesser.

28 93 number of nodes found Random graph: BFS Animation taken from a presentation by Ofrit Lesser.

29 Communities (a.k.a. clusters/modules) Community structure: the organization of vertices in clusters, with many edges joining vertices of the same community and relatively few edges joining different communities Often represent sets of actors sharing similar properties/roles.

30 Social networks Properties of on-line social networks Small-world phenomenon Power-law distribution Community structure Community detection

31 Community detection applications Clustering web clients with geographical proximity and similar access patterns cache servers positioning [Krishnamurty & Wang, SIGCOMM 2000] Clustering customers with similar interests Recommendation systems [Reddy et al., DNIS 2002] Analysing structural positions Identifying central actors and inter-community mediators …

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33 Edge-betweenness based detection A divisive method (as opposed to agglomerative methods) Look for an edge that is most between pairs of nodes o Responsible for connecting many pairs Remove edge and recalculate Newman and Girvan. Finding and evaluating community structure in networks, 2003

34 Shortest-path betweenness Compute all-pairs shortest paths For each edge, compute the number of such paths it belongs to Remove a maximum-weight edge Repeat until no edges (more on this later)

35 Shortest-path betweeness: an example

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37 Shortest-path betweeness: an example

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50 Dendrograms (hierarchical trees) A dendrogram (hierarchical tree) illustrates the output of hierarchical clustering algorithms Leaves represent graph nodes, top represents original graph As we move down the tree, larger communities are partitioned to smaller ones

51 Shortest-path betweeness: an example

52 Shortest-path betweeness: an example

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64 Evaluation: computer-generated networks Large number of graphs with 128 nodes and 4 communities of 32-nodes each Probability p in for intra-community edges Probablilty p ext for inter-community edges Chosen such that expected vertex degree is 16

65 Results (for 64-nodes networks) z in =6, z out =2

66 Evaluation: the Zachary karate club

67 Results on Zachary club network Shortest-pathShortest-path no recalculation Shortest path 2- communities partition missed just a single person! Re-calculation of betweenness essential

68 Quality functions Hierarchical clustering algorithms create numerous partitions In general, we do not know how many communities we should seek. How may we know that our clustering is good We need a quality function

69 The modularity quality function Newman and Girvan. Finding and evaluating community structure in networks, 2003 No communities in random graphs Equal probabilities for all edges Check how far intra-community and inter-community densities are from those you would expect in a random graph with identical nodes and same degree-distribution

70 The modularity quality function (con'd) Clauset, Newman and Moore. Finding community structure in very large networks, 2004 Modularity value # edges Graph adjacency matrix Degrees of nodes-pair Probability of an edge if only a function of node-degrees In-same-cluster indicator variable

71 Computer-generated networks: modularity Modularity maximized at correct partition

72 Zachary club network: modularity One of two local maxima at correct partition


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