Influence Maximization in Hypergraphs

TL;DR

This paper introduces a new heuristic algorithm, HDD, for influence maximization in hypergraphs, demonstrating superior effectiveness and efficiency over baseline methods through experiments on real and synthetic data.

Contribution

The paper proposes the Heuristic Degree Discount (HDD) algorithm for influence maximization in hypergraphs, extending methods from ordinary networks and showing improved performance.

Findings

HDD outperforms baseline algorithms in real and synthetic hypergraphs.

HDD is especially effective in hypergraphs with heterogeneous degree distribution.

Experiments confirm HDD's high effectiveness and efficiency.

Abstract

Influence maximization in complex networks, i.e., maximizing the size of influenced nodes via selecting K seed nodes for a given spreading process, has attracted great attention in recent years. However, the influence maximization problem in hypergraphs, in which the hyperedges are leveraged to represent the interactions among more than two nodes, is still an open question. In this paper, we propose an adaptive degree-based heuristic algorithm, i.e., Heuristic Degree Discount (HDD), which iteratively selects nodes with low influence overlap as seeds, to solve the influence maximization problem in hypergraphs. We further extend algorithms from ordinary networks as baselines and compare the performance of the proposed algorithm and baselines on both real data and synthetic hypergraphs. Results show that HDD outperforms the baselines in terms of both effectiveness and efficiency. Moreover, the experiments on synthetic hypergraphs indicate that HDD shows high performance, especially in hypergraphs with heterogeneous degree distribution.