Dynamic communities in referral networks

Abstract

Consider a decentralized agent-based approach for service location, where agents provide and consume services, and also cooperate with each other by giving referrals to other agents. That is, the agents form a referral network. Based on feedback from their users, the agents judge the quality of the services provided by others. Further, based on the judgments of service quality, the agents also judge the quality of the referrals given by others. The agents can thus adaptively select their neighbors in order to improve their local performance. The choices by the agents cause communities to emerge. According to our definition, an agent belongs to a community only if it has been useful to the other members of the community in prior interactions regarding a particular topic. Hence, the membership in different communities is determined based on relationships among the agents. This paper compares topic-sensitive communities of the above kind with communities as studied in traditional link analysis. It studies the correlation between the two kinds of communities as they emerge in referral networks and evaluates the two kinds of communities in terms of their effectiveness in locating service providers.