K＋＋ Shell: Influence maximization in multilayer networks using community detection

Abstract

Selecting influential users in a network is essential to spread information quickly. Identifying influential users is very useful for viral marketing and brand communication. Influence maximization (IM) is selecting a few influential users in the network who can maximize the influence spread. Many existing algorithms address IM in single-layer networks. However, the study of IM in multi-layer networks is gaining importance after the advancement and rapid growth in the usage of online social networks. Studying IM in multi-layer networks in the context of viral marketing will be interesting. Motivated by this, this paper investigates the K＋＋ Shell decomposition algorithm to find the k set of influential nodes (seed nodes) in a multi-layer network. The proposed model prunes the nodes based on degree and assign reward points to their neighbors. We conducted a comparative study of various IM algorithms and reported the results. We observed that the K＋＋ Shell decomposition algorithm outperforms other algorithms on various real-time datasets under various settings and environments.