Influential Community Search in Large Networks

Community search is a problem of finding densely connected sub- graphs that satisfy the query conditions in a network, which has attracted much attention in recent years. However, all the previ- ous studies on community search do not consider the influence of a community. In this paper, we introduce a novel community mod- el called k-influential community based on the concept of k-core, which can capture the influence of a community. Based on the new community model, we propose a linear-time online search al- gorithm to find the top-r k-influential communities in a network. To further speed up the influential community search algorithm, we devise a linear-space index structure which supports efficient search of the top-r k-influential communities in optimal time. We also propose an efficient algorithm to maintain the index when the network is frequently updated. We conduct extensive experiments on 7 real-world large networks, and the results demonstrate the ef- ficiency and effectiveness of the proposed methods.