Neighbourhood maps: decentralised ranking in small-world P2P networks

Abstract

Reputation in P2P networks is an important tool to encourage cooperation among peers. It is based on ranking of peers according to their past behaviour. In large-scale real world networks, a global centralised knowledge about all nodes is neither affordable nor practical. For this reason, reputation ranking is often based on local history knowledge available on the evaluating node. This criterion is not optimal, since it ignores useful data about interactions with other peers. We propose a simple, scalable and decentralised method, called "neighbourhood maps", that approximates rankings calculated using link-analysis techniques, exploiting the short-distance characteristics of small-world networks. We test our algorithms using data from the OpenPGP Web-of-trust, a real-world network of trust relationships.