On exploiting social relationship and personal background for content discovery in P2P networksReport as inadecuate




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1 RS2M - Département Réseaux et Services Multimédia Mobiles 2 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux 3 Departamento Ingeniería Telemática 4 R3S-SAMOVAR - Réseaux, Systèmes, Services, Sécurité SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux 5 School of Computing and Mathematics Albury Charles Sturt University

Abstract : Content discovery is a critical issue in unstructured Peer-to-Peer P2P networks as nodes maintain only local network information. However, similarly without global information about human networks, one still can find specific persons via his-her friends by using social information. Therefore, in this paper, we investigate the problem of how social information i.e., friends and background information could benefit content discovery in P2P networks. We collect social information of 384, 494 user profiles from Facebook, and build a social P2P network model based on the empirical analysis. In this model, we enrich nodes in P2P networks with social information and link nodes via their friendships. Each node extracts two types of social features - Knowledge and Similarity - and assigns more weight to the friends that have higher similarity and more knowledge. Furthermore, we present a novel content discovery algorithm which can explore the latent relationships among a node-s friends. A node computes stable scores for all its friends regarding their weight and the latent relationships. It then selects the top friends with higher scores to query content. Extensive experiments validate performance of the proposed mechanism. In particular, for personal interests searching, the proposed mechanism can achieve 100% of Search Success Rate by selecting the top 20 friends within two-hop. It also achieves 6.5 Hits on average, which improves 8x the performance of the compared methods

Keywords : Knowledge Similarity Content discovery Social P2P





Author: Xiao Han - Angel Cuevas Rumin - Noel Crespi - Rubén Cuevas Rumin - Xiaodi Huang -

Source: https://hal.archives-ouvertes.fr/



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