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Inferring Peer Centrality in Socially-Informed P2P Systems Nicolas Kourtellis, Adriana Iamnitchi Department of Computer Science & Engineering University.

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Presentation on theme: "Inferring Peer Centrality in Socially-Informed P2P Systems Nicolas Kourtellis, Adriana Iamnitchi Department of Computer Science & Engineering University."— Presentation transcript:

1 Inferring Peer Centrality in Socially-Informed P2P Systems Nicolas Kourtellis, Adriana Iamnitchi Department of Computer Science & Engineering University of South Florida Tampa, USA 11 th IEEE International Conference on Peer-to-Peer Computing Kyoto, Japan, 2011

2 Socially-aware Applications Applications collect and use social information: Location, collocation, history of interactions, etc. Build (implicit/explicit) social network of users Use: reduce spam, provide recommendations, etc. Wide range of system architectures How does the social network of users affect the load in a P2P architecture? 2 Decentralization of user social data MobiClique Yarta... PeerSoN LifeSocial.KOM Safebook Prometheus …

3 Social Graphs & P2P Networks Users connected with application-specific edges User-contributed peers form a P2P network User social graph is partitioned into subgraphs & stored on peers Questions: How do applications traverse a distributed social graph? What does it mean for the P2P routing? 3

4 Invite user Gs 2-hop hiking contacts to a trip Social graph traversals => many P2P lookups Application performance affected by projection of social graph on peers Application Example 4 => 1-hop={B, C, E} 2-hops={A, D, F, I}

5 How do the properties of the projection graph compare with the properties of the social graph projected? Projection Graph 5 Projection Graph (PG) P2P Overlay Social Graph (SG)

6 Projection Graph Model Uses: Study properties of peers such as centrality Study how the social graph topology affects P2P routing & system performance 6

7 7 Outline Motivation Projection Graph Model Social Network Centrality Metrics Degree Centrality Node Betweenness Centrality Edge Betweenness Centrality Centrality Calculation: Limitations Experimental Questions Experimental Methodology Experimental Results Impacts on Applications & Systems

8 Number of edges of a node High degree centrality peers: Network Hubs Can be targeted to directly influence many other peers with a message broadcast or distribute a search query Degree Centrality 8

9 Node Betweenness Centrality Measures the extent to which a node lies on the shortest path between two other nodes High betweenness centrality peers: Control communication between distant peers Can host data caches for reduced latency to locate data 9

10 Edge Betweenness Centrality Measures the extent to which an edge lies on the shortest path between two nodes High betweenness centrality edges: Connect distant parts of P2P network Can be monitored to block malware traffic 10

11 Calculating Peer Centrality Challenging because of: Limited access to user data (e.g., privacy settings) P2P network scale Peer churn Through experimental analysis on the social and projection graph, we investigate how to circumvent these limitations 11

12 Experimental Questions Can we approximate the centrality of peers using the centrality scores of their users? How does the number of users storing data per peer affect the centrality scores of their peers? Social graph is less dynamic than the P2P network Calculate infrequently centrality score of users & use it to estimate their peers centrality Spoiler Alert! [1, ~150] users/peer: Can estimate degree & betweenness centrality of peers with good accuracy Above 150 users/peer: The projection graph becomes highly connected => peers do not differentiate in centrality 12

13 Naturally-formed communities offer incentives for resource sharing 1 community subgraph mapped per peer Projection graphs generated from 5 real social graphs Communities detected via recursive Louvain algorithm* Varied average community size: 5,10,20,…,1000 users/peer Calculate correlation of centralities of users and their peers Compare average centralities of users and their peers Identify top centrality peers from their users scores Experimental Methodology 13 Social NetworkUsersEdges gnutella0410,87639,994 gnutella3162,561147,878 enron33,696180,811 epinions75,877405,739 slashdot82,168504,230 *V. D. Blondel et al, Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, vol. 10, 2008.

14 Correlation of Centrality Scores [1-150] users/peer: Projection graph resembles closely social graph Highest correlation of social & projection graph metrics Degree & node betweenness estimated from local information (cumulative scores) 14 After 150 users/peer: Projection graph topology loses social properties Highly connected network Peers participate equally in graph traversal Users/Peer vs. Degree Users/Peer vs. Node Betweenness Users/Peer vs. Edge Betweenness

15 Comparison of Centrality Scores Increase number of users/peer turning point in projection graph More connections with other peers increase peer degree & betweenness to maximum More social edges within peers decrease edge betweenness to minimum 15 Users/Peer vs. Degree Users/Peer vs. Node Betweenness Users/Peer Vs. Edge Betweenness

16 Finding High Betweenness Peers Placing data caches on high betweenness peers can reduce latency to locate data Can we identify such peers, knowing the top betweenness users or communities? Top 5% betweenness centrality users => top betweenness centrality peers with 80–90% accuracy 16 Users/Peer With Top-N% users With Top-N% communities

17 Summary of Findings [1, ~150] users/peer: Projection graph resembles closely social graph Highest correlation of social & projection graph metrics Degree & node betweenness can be estimated from local information (cumulative scores of users) Cannot estimate well edge betweenness Above 150 users/peer: Projection graph topology loses social properties A highly connected projection graph No differentiation in peer centrality Top betweenness centrality users can pinpoint the top betweenness centrality peers with good accuracy Overall: Applications can calculate infrequently centrality score of users to estimate peer centrality Social graph changes slowly compared to P2P network 17

18 Impact on Applications & Systems Target high degree peers to: Decrease search time Increase breadth of search and diversity of results Target high betweenness peers to: Monitor information flow and collect traces Place data caches and indexes of data location Quarantine malware outbursts Disseminate software patches Tackle P2P churn Predict centrality of peers to allocate resources Reduce overlay overhead Enhance routing tables with P2P edges for faster & more secure peer discovery 18

19 19 Thank you! This work was supported by NSF Grants: CNS and CNS


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