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# Finding Cohesive Subgroups and Relevant Members in the Nokia Friend View Mobile Social Network

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Presentation from the Social Intelligence and Networking workshop at the IEEE Social Computing conference in Vancouver, Canada on Sunday, August 30, 2009

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• Explain cohesive subgroups are groups of members that interact with each other more within the group frequently than with others outside of the group
• Explain cohesive subgroups are groups of members that interact with each other more within the group frequently than with others outside of the group
• Explain cohesive subgroups are groups of members that interact with each other more within the group frequently than with others outside of the group
• Select results in subgraph of potential members from original social graph Many types of criteria to determine whether someone is in the possible subgroups Use network centrality because
• Select results in subgraph of potential members from original social graph Choose network centrality because widely used and simple to calculate High betweenness  Good measure of subgroup clusters (eg. Tyler et al., 2005, Newman and Girvan, 2004) Degree centrality measures the number of direct connections to other nodes Betweenness centrality measures the extent to which a node can act as an intermediary or broker to other nodes Closeness centrality measures how many steps on average it takes to reach every other node in the network
• Select results in subgraph of potential members from original social graph
• Select results in subgraph of potential members from original social graph
• Explain cohesive subgroups are groups of members that interact with each other more within the group frequently than with others outside of the group
• Select results in subgraph of potential members from original social graph
• Select results in subgraph of potential members from original social graph
• Select results in subgraph of potential members from original social graph
• Explain cohesive subgroups are groups of members that interact with each other more within the group frequently than with others outside of the group
• Densely connected graph
• Top 10 people in Friend View in friends network do form a cohesive subgroup
• Select results in subgraph of potential members from original social graph
• Select results in subgraph of potential members from original social graph
• Select results in subgraph of potential members from original social graph
• Select results in subgraph of potential members from original social graph
• ### Finding Cohesive Subgroups and Relevant Members in the Nokia Friend View Mobile Social Network

1. 1. Finding Cohesive Subgroups and Relevant Members in the Nokia Friend View Mobile Social Network Alvin Chin Member of Research Staff Mobile Social Networking Group Nokia Research Center, Beijing August 30, 2009 Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
2. 2. Agenda 1. Motivation 2. Problem 3. Solution 4. Finding cohesive subgroups and important members 5. Case study: Nokia Friend View 6. Results 7. Conclusion and Future Work Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
3. 3. Many people want to be my “friend” Who should I add to my social network as friends? Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
4. 4. How can we find who are the relevant members in the social network? Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
5. 5. Solution • Create method for finding cohesive subgroups and their relevant members based on SCAN method (Chin and Chignell, 2008) • Apply it to Nokia Friend View network to see its viability and to describe its social behaviour Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
6. 6. Agenda 1. Motivation 2. Problem 3. Solution 4. Finding cohesive subgroups and important members 5. Case study: Nokia Friend View 6. Results 7. Conclusion and Future Work Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
7. 7. Cohesive subgroups • Group of members having more and frequent interactions with each other than outside group 12 15 A B A B 6 7 8 10 E C D F Clique K-plex Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
8. 8. Finding cohesive subgroups • How • Social network analysis, centrality, clustering • But no automated or systematic method • My work: modified SCAN method adapted from Chin and Chignell (2008) Chin, Alvin and Chignell, Mark(2008). Automatic detection of cohesive subgroups within social hypertext: A heuristic approach, New Review of Hypermedia and Multimedia,14:1, 121 — 143 Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
9. 9. Modified SCAN method: 1. Select • Find only potential members and remove the rest that do not meet criteria (cutoff) • Possible selection criteria • Network centrality (Frievolt and Bielikova, 2005; Newman and Girvan, 2004) • Subnet density (Herring et al, 2004) • Indegree/outdegree (Kumar et al, 1999; Ali- Hasan and Adamic, 2007) Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
10. 10. Network centrality A 15 C 8 B 11 12 6 10 6 20 8 16 10 D 6 E F 17 15 12 G H Betweenness Degree centrality Closeness centrality centrality Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
11. 11. Determining cutoff centrality Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
12. 12. Modification to original SCAN Select • Use cutoff betweenness centrality, then cutoff degree and closeness centrality • Associated with strong sense of community (Chin and Chignell, 2007) • All members whose centrality < cutoff are removed Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
13. 13. Modified SCAN method: 2. Collect • Find subgroups from interactions amongst potential members found in Select • Use weighted average hierarchical clustering • Computationally efficient than k-plex analysis (Chin and Chignell, 2007) • Output is dendrogram – set of nested, non- overlapping clusters Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
14. 14. Weighted average hierarchical clustering Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
15. 15. Agenda 1. Motivation 2. Problem 3. Solution 4. Finding cohesive subgroups and important members 5. Case study: Nokia Friend View 6. Results 7. Conclusion and Future Work Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
16. 16. Nokia Friend View on the web (friendview.nokia.com) Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
17. 17. Nokia Friend View on mobile (friendview.nokia.com) Map Status updates from Comments to status friends and me update Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
18. 18. Finding subgroups and relevant members in Friend View • Does the friend network influence the interaction network? Are the connections in the interaction network representative of the friend connections in the friend network? • Do subgroup members send more comments than others in the interaction network? • Do subgroup members have more friends than others in the interaction network? Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
19. 19. Agenda 1. Motivation 2. Problem 3. Solution 4. Finding cohesive subgroups and important members 5. Case study: Nokia Friend View 6. Results 7. Conclusion and Future Work Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
20. 20. Relevant members in friend network – top 10 Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
21. 21. Subgroups in friend network – top 10 Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
22. 22. Relevant members in interaction network – top 10 Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
23. 23. Subgroups in interaction network – top 10 Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
24. 24. Does the friend network influence the interaction network? Yes! • 9 out of top 10 in friend network are in interaction network • 5 out of top 10 in friend network are in top 10 interaction network Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
25. 25. Do subgroup members send more comments and have more friends than others in the interaction network? Yes! Feature Subgroup members Interaction network Avg # of comments 212.9 7.775 Avg # of friends 57.8 3.075 • Members that are friends of each other, are clustered together from their conversations to form subgroups • Members of cohesive subgroups interact with each other significantly more than non-subgroup members and are most likely to be friends with each other Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
26. 26. Conclusions • Described modified SCAN method for finding cohesive subgroups and relevant members • Applied method to Nokia Friend View • Discovered • Top 10 members in the interaction network are friends of each other • Subgroup members have more comments and friends Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
27. 27. Future work • Content analysis of messages in Friend View • Analyze the Friend View dataset in different time periods • Based on previous work: TorCamp study (Chin and Chignell, 2008) • Create a recommendation and ranking algorithm based on our method Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09
28. 28. friendview.nokia.com Alvin Chin – Social Intelligence and Networking workshop, IEEE SocialCom’09