CBIM: Community-based influence maximization in multilayer networks

Abstract

Selecting seed nodes (most influential nodes) in networks has attracted attention due to seed nodes’ ability to influence and spread information. Seed nodes are essential to understanding the spreading and controlling of the information dynamics of the networks. Influence maximization (IM) is predominant in monolayer networks. After the advancements and widespread usage of social networks, applying influence maximization to multilayer networks is gaining popularity. Identifying influential nodes precisely in multilayer networks is a challenging and yet unexplored task. Based on studies, individuals in a community interact frequently and are more likely to influence each other. Motivated by this observation, this paper proposes community-based influence maximization (CBIM) model to find k seed nodes in multilayer networks. CBIM has two phases: In the first phase, CBIM uses the function FIC(M) to find the small communities from a multilayer network based on dice neighborhood similarity. It uses the function CSC(CSinit,θ) to merge smaller communities and generate larger communities to improve communities’ quality. In the second phase, CBIM computes Edge Weight Sum (EWS) for each node in a community and ranks the nodes based on EWS. CBIM uses the quota-based approach to select the seed node set from the communities based on the ranks. A comparative study of various influence maximization (IM) algorithms shows that the CBIM algorithm performs better than the state-of-the-art. The simulation studies have shown that CBIM can detect a set of most influential nodes on real-time datasets under various settings and environments.