Influence Maximization in Hypergraphs by Stratified Sampling for Efficient Generation of Reverse Reachable Sets

TL;DR

This paper introduces HyperIM, a novel hypergraph influence maximization algorithm that leverages stratified sampling to efficiently generate reverse reachable sets, significantly improving speed and influence spread over existing methods.

Contribution

HyperIM uniquely incorporates hyperedge structural information with stratified sampling, enhancing efficiency and effectiveness in influence maximization on hypergraphs.

Findings

Reduces RR set requirements and runtime by orders of magnitude.

Increases influence spread by up to 2.73 times.

Demonstrates superior performance on real-world hypergraphs.

Abstract

Given a hypergraph, influence maximization (IM) is to discover a seed set containing $k$ vertices that have the maximal influence. Although the existing vertex-based IM algorithms perform better than the hyperedge-based algorithms by generating random reverse researchable (RR) sets, they are inefficient because (i) they ignore important structural information associated with hyperedges and thus obtain inferior results, (ii) the frequently-used sampling methods for generating RR sets have low efficiency because of a large number of required samplings along with high sampling variances, and (iii) the vertex-based IM algorithms have large overheads in terms of running time and memory costs. To overcome these shortcomings, this paper proposes a novel approach, called \emph{HyperIM}. The key idea behind \emph{HyperIM} is to differentiate structural information of vertices for developing stratified sampling combined with highly-efficient strategies to generate the RR sets. With theoretical guarantees, \emph{HyperIM} is able to accelerate the influence spread, improve the sampling efficiency, and cut down the expected running time. To further reduce the running time and memory costs, we optimize \emph{HyperIM} by inferring the bound of the required number of RR sets in conjunction with stratified sampling. Experimental results on real-world hypergraphs show that \emph{HyperIM} is able to reduce the number of required RR sets and running time by orders of magnitude while increasing the influence spread by up to $2.73X$ on average, compared to the state-of-the-art IM algorithms.