Exploiting semantic proximities for content search over p2p networks

In this paper, we address the issue of content search over peer-to peer networks. We use the concept of semantic proximity that exploits the commonalities of interests exhibited among peer users so as to decompose the network into semantic clusters. We initially define search entropy, as a metric indicating the average number of packets required to locate the requested content. Then, spectral clustering is used to organize the peer nodes into semantic clusters so that (a) the probability that a node locates content within its own cluster is maximized, while simultaneously; (b) the respective probability of finding this content outside this cluster is minimized. The proposed semantic partitioning algorithm is then extended into a hierarchical two-tier scheme, in which practical issues arising for the deployment of a peer-to-peer (p2p) application can be more easily addressed. After the system has been initialized, a dynamic algorithm places new users that join the p2p network into appropriately selected clusters and also handles peer departures without the need for matrix eigen decomposition process which is necessary for the assessment of the initial static partitioning. Our experimental results validate that (a) our static partitioning outperforms traditional and novel search techniques and (b) our dynamic algorithm is able to efficiently track the system’s progression maintaining the search entropy close to the initially assessed levels.