An Ant Colony Optimization Approach to Expert Identification in Social Networks
In a social network there may be people who are experts on a subject. Identifying such people and routing queries to such experts is an important problem. While the degree of separation between any node and an expert node may be small, assuming that social networks are small world networks, not all nodes may be willing to route the query because flooding the network with queries may result in the nodes becoming less likely to route queries in the future. Given this constraint and that there may be time constraints it is imperative to have an efficient way to identify experts in a network and route queries to these experts. In this paper we present an Ant Colony Optimization (ACO) based approach for expert identification and query routing in social networks. Also, even after one has identified the experts in the network, there may be new emerging topics for which there are not identifiable experts in the network. For such cases we extend the basic ACO model and introduce the notion of composibility of pheromones, where trails of different pheromones can be combined to for routing purposes.
KeywordsSocial Network Destination Node Motivation Factor Small World Network Mobile Social Network
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