Finding influential communities in massive networks

• DOI: 10.1007/s00778-017-0467-4
• Publication Date: 2017-05-30T07:14:37Z
• Authors: Rong-Hua Li; Lu Qin; Jeffrey Xu Yu; Rui Mao
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Community search is a problem of finding densely connected subgraphs that satisfy the query conditions in a network, which has attracted much attention in recent years. However, all the previous studies on community search do not consider the influence of a community. In this paper, we introduce a novel community model called k-influential community based on the concept of k-core to capture the influence of a community. Based on this community model, we propose a linear time online search algorithm 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 data structure which supports efficient search of the top-r k-influential communities in optimal time. We also propose an efficient algorithm to maintain the data structure when the network is frequently updated. Additionally, we propose a novel I/O-efficient algorithm to find the top-r k-influential communities in a disk-resident graph under the assumption of $${{\mathcal {U}}}=O(n)$$ , where $${{\mathcal {U}}}$$ and n denote the size of the main memory and the number of nodes, respectively. Finally, we conduct extensive experiments on six real-world massive networks, and the results demonstrate the efficiency and effectiveness of the proposed methods.