Efficient Algorithms for Influence Maximization in Hypergraphs by Stratified Sampling

Lingling Zhang, Tiancheng Lu, Zhi-Ping Shi, Zhiwei Zhang, Ye Yuan, Guoren Wang

Abstract: Influence maximization (IM) aims to identify $k$ vertices that maximize influence spread across a network. While well-studied in regular graphs, IM in hypergraphs presents unique challenges: conventional graph-based IM methods fail to capture hypergraph-specific structural properties, and existing hypergraph IM algorithms lack theoretical guarantees for time complexity and approximation quality. We address these gaps with HyperIM, a novel algorithm leveraging stratified sampling to generate random reversible reachable sets for efficient seed selection. Our key innovation lies in dual-perspective stratified sampling: assigning sampling probabilities based on vertex structural properties while applying size-adaptive sampling strategies. This approach optimizes seed selection, reduces computational costs, and provides rigorous theoretical guarantees. We further propose HyperIM_BRR, which optimizes the required number of reversible reachable sets, achieving substantial cost reduction without sacrificing accuracy. Extensive experiments on real-world hypergraphs demonstrate that our algorithms significantly outperform state-of-the-art methods, delivering faster execution times and superior influence spread.

External IDs:dblp:journals/tkde/ZhangLSZYW25