Influence maximization on hypergraphs via multi-hop influence estimation

Influence Maximization (IM) has promising applications in social network marketing and has been extensively researched over the past years. However, previous IM studies mainly focus on ordinary graphs rather than hypergraphs, where edges cannot accurately describe group interactions or relationships. To model group interactions, we investigate the IM problem on hypergraphs under the Susceptible–Infected spreading model with Contact Process dynamics (SICP) in this paper. In this paper, we proposed a probability distribution-based method, called Multi-hop Influence Estimation (MIE), which can accurately estimate the rank of influence expectation of nodes, to solve the IM problem on hypergraphs. Specifically, we compute the influence score for each node through a constrained Depth First Search (DFS) under a probability model, and then select seed node according to the influence score. In addition, by analysing the characteristics of the influence diffusion model, we find that the influence of a node is significantly related to its neighbourhood structure. Based on the observation, we propose a term named neighbourhood coefficient to describe the neighbourhood structure of a node. Further, an efficient and effective method, called Adaptive Neighbourhood Coefficient Algorithm (Adeff), is proposed to solve the IM problem on hypergraphs. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of our proposed methods. Compared with the state-of-the-art approach, our proposed methods can achieve up to 450% improvement in terms of effectiveness.