Link Recommendation In P2P Social Networks
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Link Recommendation In P2P Social Networks. Yusuf Aytaş, Hakan Ferhatosmanoğlu, Özgür Ulusoy Bilkent University, Ankara, Turkey. Outline. Introduction Motivation for P2P Social Networks Link Recommendation P2P Top-k Common Neighbor Experiments Discussion Future Work. Introduction.

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Link Recommendation In P2P Social Networks

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Link recommendation in p2p social networks

Link Recommendation In P2P Social Networks

Yusuf Aytaş, Hakan Ferhatosmanoğlu, Özgür Ulusoy

Bilkent University, Ankara, Turkey


Outline

Outline

  • Introduction

  • Motivation for P2P Social Networks

  • Link Recommendation

  • P2P Top-k Common Neighbor

  • Experiments

  • Discussion

  • Future Work

VLDB WOSS 2012


Introduction

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


Problems with centralized systems

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


Advantages of p2p systems

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


P2p social network challenges

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


Social network algorithms on p2p environment

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


Problem background

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


Problem background1

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


Link recommendation

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


Link recommendation cont d

Link Recommendation(Cont’d)

5

9

2

23

VLDB WOSS 2012


Node scoring

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


Top k common neighbor

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


Top k fa and ta common neighbor

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


Setup for experiments

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


Experiments performance

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


Top k ta vs top k fa

Top-K TA vs. Top-K FA

VLDB WOSS 2012


Experiments accuracy

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


Top k ta vs top k fa1

Top-K TA vs. Top-K FA

VLDB WOSS 2012


Sowhoo

SOWHOO

  • We are building a P2P Social Network application to test our algorithms.

Super Peer

Super Peer

VLDB WOSS 2012


Sowhoo cont d

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


Discussion

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


Future work

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


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