Link Recommendation In P2P Social Networks

1. Link Recommendation In P2P Social Networks Yusuf Aytaş, Hakan Ferhatosmanoğlu, Özgür Ulusoy Bilkent University, Ankara, Turkey

2. Outline • Introduction • Motivation for P2P Social Networks • Link Recommendation • P2P Top-k Common Neighbor • Experiments • Discussion • Future Work VLDB WOSS 2012

3. Introduction • Social networks are mostly based on centralized infrastructure (“fat server thin client”). • However, P2P infrastructure is a natural alternative for social networks. • Problems with centralized infrastructure. VLDB WOSS 2012

4. Problems with Centralized Systems • Privacy: Social network providers can misuse users’ data. • Censorship: Social network provider can censor users’ shares. • Scalability: Data can be distributed over network. • These can be avoided in P2P Social networks. VLDB WOSS 2012

5. Advantages of P2P Systems • Data can be maintained by peers, no need for another computer. • Level of privacy can be defined according to user. • Misuse of both linkage and user data is prevented. • Accordingly, significant amount of research is needed for algorithms and systems of P2P Social Networks. VLDB WOSS 2012

6. P2P Social Network Challenges • Algorithm Perspective • Distributed graph algorithms • P2P Performance • Systems Perspective • Storage • Robustness • Security • SOWHOO: Our open source implementation • https://github.com/yusufaytas/sowhoo VLDB WOSS 2012

7. Social Network Algorithms on P2P Environment • In a P2P Social Network, peers have limited information about the network. • Known algorithms like link prediction, community detection, information diffusion should be revisited. • Efficiency of overlay network should be taken into account as well as algorithm accuracy. • In this context, we propose a new approach “Link Recommendation”. VLDB WOSS 2012

8. Problem Background • Common Neighbor : A node is more likely to interact with another node if number of their shared neighbors is high. • Top-K Query Processing: Finding k objects that have highest scores. 0.23 0.27 0.41 0.34 VLDB WOSS 2012

9. Problem Background • Zhang proposed a Common Neighbor algorithm (NCNP) to predict links in a distributed graph. • Kermarrec proposed a distributed social graph embedding algorithm (SocS) for link prediction. • We consider P2P environment settings. • Our approach uses P2P Top-k retrieval to enhance performance. • Scoring methods improve network overlay. VLDB WOSS 2012

10. Link Recommendation • Link recommendation: suggesting new links by considering both neighborhood information and network performance. • To measure social information and P2P network, we use node scoring. • We adapted Common Neighbors to distributed environment using Fagin’s and Threshold Algorithm. VLDB WOSS 2012

11. Link Recommendation(Cont’d) 5 9 2 23 VLDB WOSS 2012

12. Node Scoring • Node Importance • Reputation Scoring • P2P Systems Measures • Composite Measures • Trusted Centrality • Available Authority • Our weighting strategy may suggest friendships that improve P2P Topology VLDB WOSS 2012

13. Top-K Common Neighbor E B F Node A requests new Recommended Node. Each node returns recommended node. A Node A evaluates returned nodes and terminates if algorithm converges. C D VLDB WOSS 2012

14. Top-K FA and TA Common Neighbor • Top-K FA Common Neighbor algorithm stops if it receives k recommended nodes from all neighbors. • It generally results in worst case scenario. • Top-K TA Common Neighbor algorithm stops if it has k recommended nodes greater than the threshold(approximated). • Threshold calculated at each iteration. VLDB WOSS 2012

15. Setup For Experiments • Synthetic and real data • For real data • Gnutella (6301 nodes and 20777 edges) • Wikipedia (7115 nodes and 103689 edges) • For synthetic data, we implemented: • Uniformly distributed model, • Small world model of Watts and Strogatz, • Clustering model of Holme and Kim. • We plan to use data from SOWHOO. VLDB WOSS 2012

16. Experiments(Performance) • We have evaluated algorithms’ efficiency as number of interactions vs. number of edges. • An interaction/access is to retrieve recommended node information, i.e. weight and address from a peer. • Assigned weights to network globally and locally according to power-law and uniform distribution. • Global weights are single and do not change according to a node. Local weights are assigned by each node and differ. VLDB WOSS 2012

17. Top-K TA vs. Top-K FA VLDB WOSS 2012

18. Experiments (Accuracy) • We evaluated algorithms according to recommended nodes by considering regular Common Neighbor as baseline. • Also need to evaluate by using: • Rank of recommended nodes. • Sum of weights for recommended nodes. • Performance measure(ω) for accuracy and efficiency trade-off: VLDB WOSS 2012

19. Top-K TA vs. Top-K FA VLDB WOSS 2012

20. SOWHOO • We are building a P2P Social Network application to test our algorithms. Super Peer Super Peer VLDB WOSS 2012

21. SOWHOO(Cont’d) • SOWHOO has 3 layers : application layer, system layer, and network layer. • Application Layer handles user requests and provides user interface. Application Layer System Layer • System Layer provides mechanisms like pub/sub, notify/update and so on. Network Layer • Network layer provides messaging infrastructure between peers. VLDB WOSS 2012

22. Discussion • We presented ongoing work on Link Recommendation. • P2P Top-K FA and TA Common Neighbors to find recommended links for a node. • P2P Top-k TA is significantly better than P2P Top-k FA Common Neighbors in terms of efficiency. • We also presented weighting methods and proposed combined weights. VLDB WOSS 2012

23. Future Work • We are planning to improve Top-K TA Common Neighbor algorithm to Top-K TA Common Neighbor+. • Test our algorithms according to accuracy measures we have discussed. • We are planning to complete implementation of SOWHOO. • Test our algorithms on data generated by SOWHOO. VLDB WOSS 2012