Influence Maximization in Hypergraphs Using A Genetic Algorithm with New Initialization and Evaluation Methods

TL;DR

This paper presents a novel genetic algorithm approach for influence maximization in hypergraphs, incorporating new initialization, evaluation, and mutation techniques to better capture high-order interactions and improve influence spread.

Contribution

It introduces a hypergraph-independent cascade model and a GA-based method with enhanced initialization and mutation to effectively identify influential nodes in hypergraphs.

Findings

Outperforms existing methods in synthetic hypergraph experiments

Effectively captures high-order interactions in influence spread

Demonstrates improved influence maximization on real hypergraphs

Abstract

Influence maximization (IM) is a crucial optimization task related to analyzing complex networks in the real world, such as social networks, disease propagation networks, and marketing networks. Publications to date about the IM problem focus mainly on graphs, which fail to capture high-order interaction relationships from the real world. Therefore, the use of hypergraphs for addressing the IM problem has been receiving increasing attention. However, identifying the most influential nodes in hypergraphs remains challenging, mainly because nodes and hyperedges are often strongly coupled and correlated. In this paper, to effectively identify the most influential nodes, we first propose a novel hypergraph-independent cascade model that integrates the influences of both node and hyperedge failures. Afterward, we introduce genetic algorithms (GA) to identify the most influential nodes that leverage hypergraph collective influences. In the GA-based method, the hypergraph collective influence is effectively used to initialize the population, thereby enhancing the quality of initial candidate solutions. The designed fitness function considers the joint influences of both nodes and hyperedges. This ensures the optimal set of nodes with the best influence on both nodes and hyperedges to be evaluated accurately. Moreover, a new mutation operator is designed by introducing factors, i.e., the collective influence and overlapping effects of nodes in hypergraphs, to breed high-quality offspring. In the experiments, several simulations on both synthetic and real hypergraphs have been conducted, and the results demonstrate that the proposed method outperforms the compared methods.