Analysis of Fusing Online and Co-presence Social Networks

1. Juan (Susan) Pan, Daniel Boston, and CristianBorcea Department of Computer Science New Jersey Institute of Technology Analysis of Fusing Online and Co-presence Social Networks

2. Pervasivesocial applications • Location-aware social apps • Traditional social apps • Socially-aware apps • BUBBLE Rap • Use social knowledge to improve packet forwarding in delayed tolerant networks • Tribler • Use social knowledge to reduce peer-to-peer communication overhead

3. Social information collection • Declared by users • Implicitly, through online social networks • Explicitly, through surveys • Extracted from user online interactions • Extracted from user mobility traces • Location traces • Co-presence traces (e.g., using Bluetooth)

4. Social information representation • Multiple social graphs (e.g., Facebook and co-presence) • Vertices -> users • Edges -> social ties • Online social networks (OSN) provide relatively stable social graph • Many connections are weak • Example: actors have millions of “friends” • Not all social contacts use OSN apps • Co-presence social network (CSN) identifies social ties grounded on real-world interactions • Hard to differentiate social connections from passers-by

5. Research questions • Do OSN and CSN just reinforce each other or capture different types of social ties? • Can a fused network take advantage of the strengths of both? • How can we quantify the benefits of this fusion? • Can we measure the contribution of each source network to the fused network?

6. Outline • Motivation • Data collection • Social graph representation • Analysis of global network parameters • Analysis of local network parameters • Conclusions

7. Study participants • One month of CSN data and Facebook data for the same set of 104 students • Volunteers • Received compensation • Belong to various departments at NJIT

8. A B C Bluetooth based co-presence data INTERNET

9. Co-presence statistics

10. Facebook data • Subjects gave us permission to collect data • Friends, wall writings, comments, photo tags • Online interaction is wall writing, comment or photo tag • Count number of interactions between user pairs

11. Outline • Motivation • Data collection • Social graph representation • Analysis of global network parameters • Analysis of local network parameters • Conclusions

12. Weighted social graphs are more accurate • OSN:Weightonline= number of interactions • CSN:Weightco-presence = 0.5 хWeightduration+ 0.5 хWeightfrequency • How to make OSN and CSN weights comparable? • Need weight normalization • OSN: Weightonline[1,40] • CSN • Weightduration= (Duration/MAXduration)*40 [1,40] • Weightfrequency= (Frequency/MAXfrequency)*40 [1,40]

13. CSN noise reduction • How to remove edgesdue to passers-by in CSN? • Very short and infrequent co-presence does not indicate the presence of a social tie • Find duration & frequency thresholds for adding a CSN edge • Increase thresholds until Edit distance between CSN and OSN stabilizes • Edit distance: number of edge additions/deletions to transform one graph into the other • Keep OSN unchanged because Facebook friendship confirmations validate social ties

14. Threshold selection Total meeting duration threshold α= 160 minutes per month Total meeting frequency threshold β= 3 times per month

15. Resulting social graphs Online Social Network Co-presence Social Network Fused Network (51 shared edges)

16. Outline • Motivation • Data collection • Social graph representation • Analysis of global network parameters • Degree, connectivity, centrality, cohesiveness • Analysis of local network parameters • Conclusions

17. Degreedistribution 3 nodes are social butterflies • OSN degree follows proximately power law distribution • CSN degree does not resemble as strong power-law distribution as OSN’s • Due to meeting with familiar strangers • Consequently, similar result observed for fused network • Most nodes have high degree in either CSN or OSN, but not both • 3 nodes have high degree in both CSN and OSN • Increased average degree means people meet different sets of contacts in the two source networks

18. Connectivity CSN contributes 27% more edges than OSN • Compared to OSN, CSN has 55% more connected people • Almost all people connected in fused network • Average weighted shortest path reduced in fused network • Stronger social connectivity: reason to leverage it in social apps

19. OSN CSN Betweenness centrality and cluster coefficient • CSN has much longer average shortest path than OSN • Hence, average betweenness is high • In fused network, average shortest path is low, but betweenness is highest • Social centrality is improved • Average edge weight shows that people interact more in real life than online • Highly socially active person online is not necessarily highly socially active in real life • Thus, smaller values in fused network • OSN has higher cohesiveness • People become friends when sharing common friends • OSN contributes more to fused

20. Outline • Motivation • Data collection • Social graph representation • Analysis of global network parameters • Analysis of local network parameters • Node, edge, community • Conclusions

21. Similarity of node degree and edge weight • Calculate Euclidean distance of the degree vector (104 nodes) and shared edge weight vector (51 edges) • Similarity is inverse of distance • CSN more similar to fused network

22. Computation of community similarity • How to quantify community similarity across networks? • Few communities are the same • Better to quantify community overlapping • Compute k-clique overlapping clusters on the three networks separately • Use community overlapping matrix to compute distance between networks (inverse of similarity)

23. Community similarity • Fused network has larger average size community than OSN and CSN (fused=6.1, CSN=4.9, OSN=5.2) • CSN is closer to the fused network for weaker communities (k=3,4) • OSN is closer to fused network for stronger communities(k=5) • OSN contributes stronger social communities than CSN

24. Conclusions • CSN and OSN represent two different classes of social engagement • Applications may benefit from fused network that merges CSN and OSN • CSN increases the fused network connectivity and communication strength • OSN strengthens the community structure and lowers the average path length of fused network • Typical example is friend-of-friend apps

25. Mobius project • Decentralized two-tier infrastructure for mobile social computing • P2P tier • Collects on-line social information • Manages social state • Runs user-deployed services to support mobile apps • Dynamically adapts to geo-social context • Energy-efficiency, scalability, reliability • Mobile tier • Runs mobile applications • Collects geo-social information from phones Application scenario: community multimedia sharing system

26. Acknowledgment: NSF Grant CNS-0831753 http://www.cs.njit.edu/~borcea/mobius/ Thank you!

27. Related work • Kostakos[2010] • The networks are very sparse • Co-presence social ties are based on only one meeting • Does not consider user interaction (edge weight) • There is no proper noise reduction • Eagle[2009], Cranshaw[2010] • Focused on using co-presence data to predict friendship • Mtibaa[2008] • Concluding that the two graphs are similar • Conference over a single day • These results cannot be broadened

28. Power Law distribution • Node degrees in real-world large scale social networks often follow a power law distribution • few nodes with many degrees and many others with few degrees