DCDIM: Diversified influence maximization on dynamic social networks

Abstract

The problem of influence maximization (IM) identifies the top $k$ nodes that can maximize the expected influence in social networks. IM has many applications, such as viral marketing, business strategy, and profit maximization. However, most of the existing studies focus on maximizing the influenced node in the static social network, and overlook the diversity of the influenced nodes. To address this issue, this work proposes a framework to diversify the influenced node in dynamic social networks. Utilizing the framework, our DCDIM algorithm identifies the communities dynamically and maximizes the communities of influential nodes using a proposed objective function. We prove that the proposed objective function is Monotonic, Submodular, and NP-Hard. The experiments have been conducted on four datasets, and the experimental results show that the proposed approach achieves the maximum number of communities and gives competitive influenced node with the benchmark algorithms.