Community Quality and Influence Maximization: An Empirical Study
Motaz Ben Hassine
TL;DR
The study investigates how the quality of detected communities impacts influence diffusion under the Independent Cascade model and proposes extending α-Hierarchical Clustering to Influence Maximization (α-HCIM). It introduces a propagator-score and a two-stage seed selection that leverages overlapping nodes and large communities, comparing against HCIM. Across real-world networks, higher-quality communities yield greater diffusion, especially at low propagation probabilities, highlighting the practical value of community quality in seed selection. The findings guide seed-selection strategies and motivate exploring additional diffusion models in future work.
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 $α$-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 $α$-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.