Modularity-based approach for tracking communities in dynamic social networks

TL;DR

This paper presents a modularity-based framework for tracking evolving communities in dynamic social networks, effectively identifying significant events without predefined thresholds, and demonstrating superior performance on synthetic and real-world Twitter data.

Contribution

Introduces a novel, threshold-free, modularity-based framework for dynamic community tracking that outperforms existing methods and is applicable to large-scale social networks.

Findings

Framework outperforms state-of-the-art methods on synthetic data.

Successfully applied to Twitter data with over 60,000 users.

Effectively identifies significant community events over time.

Abstract

Community detection is a crucial task to unravel the intricate dynamics of online social networks. The emergence of these networks has dramatically increased the volume and speed of interactions among users, presenting researchers with unprecedented opportunities to explore and analyze the underlying structure of social communities. Despite a growing interest in tracking the evolution of groups of users in real-world social networks, the predominant focus of community detection efforts has been on communities within static networks. In this paper, we introduce a novel framework for tracking communities over time in a dynamic network, where a series of significant events is identified for each community. Our framework adopts a modularity-based strategy and does not require a predefined threshold, leading to a more accurate and robust tracking of dynamic communities. We validated the efficacy of our framework through extensive experiments on synthetic networks featuring embedded events. The results indicate that our framework can outperform the state-of-the-art methods. Furthermore, we utilized the proposed approach on a Twitter network comprising over 60,000 users and 5 million tweets throughout 2020, showcasing its potential in identifying dynamic communities in real-world scenarios. The proposed framework can be applied to different social networks and provides a valuable tool to gain deeper insights into the evolution of communities in dynamic social networks.