Expertise Discovery in Decentralised Online Social Networks

Abstract

Distributed Social Networks (DSNs) are the solution to the privacy and security problems of online social networks. In DSN, a user controls their own data as it chooses personal storage for its social network data. In absence of a centralized entity with access to all social network data, information retrieval becomes difficult in DSNs. In this paper we propose to use crowd sourcing for information retrieval in a DSN. We analyze a popular information retrieval problem called expert search in a social network. In this paper, we present an algorithm for such a crowd sourcing based search process which includes solution for (a) the worker selection problem (b) the task selection problem and (c) the reward distribution problem. Using experimental evaluation, we show that, the search algorithms proposed in this paper can be as efficient as a greedy search algorithm with access to entire social network information.