Multiplex network influence maximization based on representation learning method

Abstract

Influence maximization based on representation learning has garnered significant attention in recent years, with numerous studies focusing on monolayer networks. However, given the inherent complexity and multiplicity of social networks, addressing the Multiplex network Influence Maximization (MIM) problem is more practical. The MIM problem aims to find a set of seed nodes to maximize the spread of influence throughout the multiplex network. To tackle this issue, this paper introduces a reverse random walk centrality method based on multiplex network representation learning. This method leverages multiplex network representation learning to derive node embeddings across different layers of the network. By calculating similarity weights between nodes within each layer, a reverse random walk is performed to quantify node importance based on the frequency of visits. The top-k nodes with the highest visit counts are then selected as seed nodes. Both single influence propagation and a coupled spread model that integrates competitive and cooperative influence dynamics are considered. Extensive experiments on several real-world datasets demonstrate that the proposed method outperforms existing techniques in terms of effectiveness, providing robust seed node selection for influence maximization. These findings highlight the efficiency and applicability of the proposed method for practical multiplex network scenarios.