The success of a P2P file-sharing network highly depends on the scalability and versatility of its search mechanism. Two particularly desirable search features are scope (ability to find infrequent items) and support for partial-match queries (queries that contain typos or include a subset of keywords). While centralized-index architectures (such as Napster) can support both these features, existing decentralized architectures seem to support at most one: prevailing unstructured P2P protocols (such as Gnutella and FastTrack) deploy a "blind" search mechanism where the set of peers probed is unrelated to the query; thus they support partial-match queries but have limited scope. On the other extreme, the recently-proposed distributed hash tables (DHTs) such as CAN and CHORD, couple index location with the item's hash value, and thus have good scope but can not effectively support partial-match queries. Another hurdle to DHTs deployment is their tight control of the overlay structure and the information (part of the index) each peer maintains, which makes them more sensitive to failures and frequent joins and disconnects.

Abstract — P2P systems are a great solution to the problem of distributing resources. The main issue of P2P networks is that searching and retrieving resources shared by peers is usually expensive and does not take into account similarities among peers. In this paper we present preliminary simulations of PROSA, a novel algorithm for P2P network structuring, inspired by social behaviours. Peers in PROSA self–organise in social groups of similar peers, called “semantic–groups”, depending on the resources they are sharing. Such a network smoothly evolves to a small–world graph, where queries for resources are efficiently and effectively routed. I.

Abstract. P2P overlay networks have been deeply studied in the last few years. The main problems of such networks are resources distribution and retrieving. In this paper PROSA is presented. It is based on a novel adaptive algorithm to build an efficient and semantically searchable P2P system. This algorithm is inspired by human relationships, since social communities possess some interesting properties (such as being “small–worlds”) that allow fast and efficient routing of queries for resources. 1

Peers in peer-to-peer file-sharing systems use conjunctive queries as a way of controlling query cost in the absence of information about the behavior of other peers. Conjunctive queries are good at increasing the precision of query result sets, but may be overly selective, decreasing overall performance. We consider relaxing the conjunctive matching criterion and its impact on performance and cost. Experimental results indicate that significant performance improvements are possible at reasonable cost. 1.

Abstract. PROSA tries to simulate the evolution of human relationships from simple acquaintance to friendship and partnership. Our target is to obtain a self– reconfiguring P2P system which possesses some of the desirable features of social communities. We show that PROSA naturally evolves into a small–world network, with an high clustering coefficient and a relly short average path length. 1

Abstract — In this paper we present a formal description of PROSA, a P2P resource management system heavily inspired by social networks. Social networks have been deeply studied in the last two decades in order to understand how communities of people arise and grow. It is a widely known result that networks of social relationships usually evolves to small–worlds, i.e. networks where nodes are strongly connected to neighbours and separated from all other nodes by a small amount of hops. This work shows that algorithms implemented into PROSA allow to obtain an efficient small–world P2P network. We also show how taking advantage of PROSA structure it is possible to effectively answer queries. In particular, the so–called query recall for PROSA is estimated and compared to that obtained in SETS [1] and GES [2].

Abstract—To improve the performance of similarity search and information retrieval is an important research issue in peer-to-peer environment. In this paper, we propose a distributed architecture for enhancing the performance of similarity search in unstructured P2P networks. The key component of the proposed architecture is a distributed, content-based, heuristic feedback mechanism, which allows peers to keep track of recent queries and learn from the assessment of answers to previous queries, so as to selfadaptively route the subsequent query to the most relevant nodes which are responsible for the query. Therefore a high recall rate can be achieved by probing only a small amount of peers. We also propose a distributed automatic query expansion mechanism to improve the quality of query results. Since the architecture is entirely distributed, it scales well with the large sized networks. The experimental results show that our architecture can efficiently solve queries with a relatively small cost. Keywords-similarity search; unstructured; peer-to-peer systems; I.

Abstract—Leveraging the state-of-the-art Information Retrieval (IR) algorithms and relevance ranking algorithm, we propose an architecture for efficient search in peer-to-peer networks. The key idea is to employ a distributed, contentbased algorithm to arrange the participating nodes into semantic clusters, in which most connections are short-distance, connecting pairs of nodes with similar contents. Nodes in different clusters are connected through long-distance links. Queries are routed to the cluster which has the most relevance contents and flooded to the nodes in the corresponding clusters. The experiment results show that the proposed architecture can achieve high recall rate with small cost of network traffic and query processing load. I.

Efficient searching is one of the important design issues in peer-to-peer (P2P) networks. Among various searching techniques, semantic-based searching has drawn significant attention recently. Gnutella-like efficient searching system (GES) [18] is such a system. GES derives a node vector, a semantic summary of all of the documents on a node, based on vector space model (VSM). The topology adaptation algorithm and search protocol are then designed according to the similarity between node vectors of different nodes. However, although GES is suitable when the distribution of documents in each node is uniform, it may not be efficient when the distribution is diverse. When there are many categories of documents at each node, the node vector representation may be inaccurate. We extend the idea of GES and present a class-based semantic searching system (CSS). It makes use of a data clustering algorithm, online spherical kmeans clustering (OSKM) [16], to cluster all documents on a node into several classes. Each class can be viewed as a virtual node. Virtual nodes are connected through virtual links. As a result, class vector replaces node vector and plays an important role in the class-based topology adaptation and search process, which makes CSS very efficient. Our simulation using the IR benchmark TREC collection demonstrates that CSS outperforms GES in terms of higher recall, higher precision and lower search cost.