Influential community search in large networks

Abstract

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, which can capture the influence of a community. Based on the new 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 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 efficiency and effectiveness of the proposed methods.

Query Optimal k-Plex Based Community in Graphs

“…We first give an example of FRE and formal definition of it based on [18], and then we show the formulation of MCKPQ can avoid free rider effect. Minimum degree dðHÞ is commonly used as community measurement in [4,11,22,28], the bigger, the better. Take graph G in Fig.…”

“…K-truss model is further improved by restricting community diameter, and avoiding free rider effect in [18]. k-core based (minimum degree) model is also used to define community in [4,11,22,28]. Sozio and Gionis [28] first introduce minimum degree as the goodness function of community and give a linear time algorithm for unbounded size problem.…”

Identification of top-K influential communities in big networks

“…Similar to our work, they took into consideration the edge structure; in addition, they used the greedy algorithm for modularity optimization. Li et al [5] used another approach, which was to study the flooding time, which is the time taken for the information to spread from one node/ community to the other node/community. In this approach, processes were considered in which the topology of the graph at time t depended only on their topology at t-1.…”

Query-Driven Maximum Quasi-Clique Search

“…Our problem bears some similarity to community search in social networks [19,4,12,20]. However, the definition of communities in the literature is not based on quasi-cliques.…”

“…However, the definition of communities in the literature is not based on quasi-cliques. E.g., Li et al [12] define a community as a connected maximal k-Core. Tsourakakis et al [20] proposed a new subgraph called optimal quasiclique, which is defined as the subgraph G i (V i , E i ) that maximizes |E i | − λ|Vi|(|Vi|−1) 2 , but G i cannot guarantee that K(G i ) ≥ λ(|V i | − 1) while the λ-quasi-clique does.…”