Community Quality and Influence Maximization: An Empirical Study

TL;DR

This study investigates how the quality of detected communities in social networks impacts influence spread, demonstrating that higher-quality communities significantly enhance diffusion effectiveness, especially at low propagation probabilities.

Contribution

It extends a community detection method to influence maximization, showing that community quality critically affects influence spread under the Independent Cascade model.

Findings

Higher-quality community structures improve influence diffusion.

Community quality has a greater impact at low propagation probabilities.

The proposed method outperforms standard clustering approaches.

Abstract

Influence maximization in social networks plays a vital role in applications such as viral marketing, epidemiology, product recommendation, opinion mining, and counter-terrorism. A common approach identifies seed nodes by first detecting disjoint communities and subsequently selecting representative nodes from these communities. However, whether the quality of detected communities consistently affects the spread of influence under the Independent Cascade model remains unclear. This paper addresses this question by extending a previously proposed disjoint community detection method, termed $\alpha$-Hierarchical Clustering, to the influence maximization problem under the Independent Cascade model. The proposed method is compared with an alternative approach that employs the same seed selection criteria but relies on communities of lower quality obtained through standard Hierarchical Clustering. The former is referred to as Hierarchical Clustering-based Influence Maximization, while the latter, which leverages higher-quality community structures to guide seed selection, is termed $\alpha$-Hierarchical Clustering-based Influence Maximization. Extensive experiments are performed on multiple real-world datasets to assess the effectiveness of both methods. The results demonstrate that higher-quality community structures substantially improve information diffusion under the Independent Cascade model, particularly when the propagation probability is low. These findings underscore the critical importance of community quality in guiding effective seed selection for influence maximization in complex networks.