A universal meta-heuristic framework for influence maximization in hypergraphs

TL;DR

This paper introduces a versatile hyper genetic algorithm (HGA) for influence maximization in hypergraphs, effectively handling higher-order interactions and outperforming baseline methods across various synthetic and real-world networks.

Contribution

It presents a novel meta-heuristic framework, HGA, specifically designed for influence maximization in hypergraphs, incorporating multiple topological features for seed node selection.

Findings

HGA achieves universal and plausible performance across different hypergraph models.

The solution of HGA is distinct from prior methods, indicating a different optimization approach.

Effective seed node selection requires combining multiple topological features.

Abstract

Influence maximization (IM) aims to select a small number of nodes that are able to maximize their influence in a network and covers a wide range of applications. Despite numerous attempts to provide effective solutions in ordinary networks, higher-order interactions between entities in various real-world systems are not usually taken into account. In this paper, we propose a versatile meta-heuristic approach, hyper genetic algorithm (HGA), to tackle the IM problem in hypergraphs, which is based on the concept of genetic evolution. Systematic validations in synthetic and empirical hypergraphs under both simple and complex contagion models indicate that HGA achieves universal and plausible performance compared to baseline methods. We explore the cause of the excellent performance of HGA through ablation studies and correlation analysis. The findings show that the solution of HGA is distinct from that of other prior methods. Moreover, a closer look at the local topological features of the seed nodes acquired by different algorithms reveals that the selection of seed nodes cannot be based on a single topological characteristic, but should involve a combination of multiple topological features to address the IM problem.