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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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slide1

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

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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