Influence maximization in hypergraphs: A self-optimizing algorithm based on electrostatic field.

Multi-individual interactions are ubiquitous in the real world, which are usually modeled by hypergraphs. Similar to low-order networks, there are special key nodes in high-order networks that are highly influential and play a crucial role in information dissemination. The objective of the influence maximization problem is to find an optimal set of nodes that maximizes the influence of the network, while maximizing influence in hypergraphs has been neglected so far, and how to explore higher-order interactions from multiple perspectives and exploit the features of higher-order structures are still open questions. To solve the above problems, a self-optimization algorithm based on electrostatic field is proposed. The network is innovatively viewed as an electrostatic field to uncover the interaction forces of the nodes in it. The improved localized Physarum polycephalum algorithm is used to assign values to the initial charges of the nodes. The effective distance among hyperedges and nodes is redefined. Furthermore, the self-optimizing update algorithm is designed to reduce the overlap of nodes' influence ranges in the optimal set. Percolation, SIR, Top-k, and correlation experiments are conducted on eight real-world networks, and the superiority of our proposed algorithm in this paper is verified by comparing with six algorithms. • A charge initialization rule based on localized Physarum polycephalum is designed. • The distances between hyperedges and between nodes are effectively measured on a hypergraph. What's more, the global and local information in the hypergraph is combined using Coulomb's law. • In order to reduce the overlap of impact areas in the seed set, a self-optimizing update algorithm is proposed to find the node that currently has the greatest impact on the network. [ABSTRACT FROM AUTHOR]