A Centrality Measure for Influence Maximization Across Multiple Social Networks

Abstract

Influence maximization (IM) is the problem of sub set selection which selects a subset of k users from the network to maximize the aggregate influence spread in the network. The paper addresses IM problem across multiple social networks simultaneously. We propose a new centrality measure to identify the most influential users and adopt the independent cascade model for information dissemination. The experiment results show the advantage of the proposed framework over classical influence maximization frameworks. The results also show the superiority of the proposed centrality measure over the state-of-the-art centrality measures. © 2019, Springer Nature Singapore Pte Ltd.