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.

Automated summary

This paper proposes a self-optimization algorithm based on electrostatic field to solve the influence maximization problem in high-order networks. By treating the network as an electrostatic field, the interaction forces between nodes are revealed. Experimental results demonstrate the superiority of the proposed algorithm over six other algorithms on real-world networks.