LiPS: Efficient P2P Search Scheme with Novel Link Prediction Techniques

The usability of peer-to-peer (P2P) file sharing systems highly depends on their search (or query, content routing) efficiency. In this paper, we present LiPS: an efficient P2P search scheme with novel link prediction techniques. LiPS is a natural combination of recent technical thrusts from two different disciplines, namely 1) the exploitation of user interests in P2P search field, and 2) the link prediction in the complex networks field. Based on the experiential observation that people´s social circle typically expands through friends´ friends, we propose a novel neighbors´ common neighbor link predictor (NCNP) and its two optimized variations. Trace-driven simulation results demonstrate the proposed link predictors and the effectiveness of LiPS. Specifically, the proposed refined and popularity-aware NCNP algorithm can double or even triple the prediction accuracy, as compared with normal common neighbor predictor. LiPS also significantly outperforms (by as large a margin as 15%) the original Shortcuts search method [1] and achieves up to 60% hit rate. Meanwhile, the query traffic is also slightly reduced.