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Peer-to-Peer (P2P) networks establish loosely coupled application-level overlays on top of the internet to facilitate efficient sharing of resources. They can be roughly classified as either structured or unstructured networks. Without stringent constraints over the network topology, unstructured P2P networks can be constructed very efficiently and are therefore considered suitable to the internet environment. However, the random search strategies adopted by these networks usually perform poorly with a large network Size. In this paper, the authors seek to enhance the search performance in unstructured P2P networks through exploiting users' common interest patterns captured within a probability-theoretic framework based on DHT.
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