1. Juan (Susan) Pan, Daniel Boston, and Cristian Borcea Department of Computer Science New Jersey Institute of Technology

2.  Traditional social apps  Location-aware 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 2

3.  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) 3

4.  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 4

5.  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? 5

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

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

8. UserSeenTime AB1:00 BA1:05 INTERNET AB1:07 8 A BC BC1:05 AC1:07

9. 9

10.  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 MaxMeansStandard Dev. Online Interactions 4024 10

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

12.  OSN: Weight online = number of interactions  CSN: Weight co-presence = 0.5 х Weight duration + 0.5 х Weight frequency 12

13.  How to remove edges due to passers-by in CSN?  Very short and infrequent co-presence does not indicate the presence of a social tie 13  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. 14 Total meeting duration threshold α= 160 minutes per month Total meeting frequency threshold β= 3 times per month

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

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

17. OSNCSNFusedCorrelation (online, co-presence) = 0.202 Average degree3.173.775.96 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 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 3 nodes are social butterflies  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 17

18. OSNCSNFusedWeighted Number of edges165196310N Size of LCC (largest connected component) 638498N Diameter of LCC787N Average length of shortest path12.321.988.77Y CSN contributes 27% more edges than OSN Compared to OSN, CSN has 55% more connected people Almost all people connected in fused network 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 Average weighted shortest path reduced in fused network Stronger social connectivity: reason to leverage it in social apps 18

19. OSNCSNFusedWeighted Average weight betweenness49.190.1394.83Y Average length of shortest path12.321.988.77Y Average edge weight3.023.641.95Y Average weighted cluster coefficient 0.1560.1220.157Y 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 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 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 OSN has higher cohesiveness People become friends when sharing common friends OSN contributes more to fused 19 OSNCSN

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

21.  Calculate Euclidean distance of the degree vector (104 nodes) and shared edge weight vector (51 edges)  Similarity is inverse of distance Distance(OSN, CSN)Distance(OSN, fused) Distance(CSN, fused) Weighted node degree 0.5580.3060.256 Node degree0.3990.3050.225 Edge weight0.5600.3240.295 21  CSN more similar to fused network

22.  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) 22

23. K=3K=4K=5 Dist(OSN, fused)256114226.5 Dist(CSN, fused)228913532.0  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 23

24.  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 24

25.  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 25 Application scenario: community multimedia sharing system

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

27.  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 27

28.  We use the method proposed by Barrat et al. (2004)[7] generalized local clustering coefficient. In this weighted version, the contribution of each triangle is weighted by a ratio of the average weight of the two adjacent edges of the triangle to the average weight of node i. The formula is is the non-weighted degree of the node i. 28

29.  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 29