# Modularity-based approach for tracking communities in dynamic social networks

02/24/2023

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by [Michele Mazza, et al.](/content/profile/michele-mazza/index.html)

Community detection is a fundamental task in social network analysis. Online
social networks have dramatically increased the volume and speed of
interactions among users, enabling advanced analysis of these dynamics. Despite
a growing interest in tracking the evolution of groups of users in real-world
social networks, most community detection efforts focus on communities within
static networks. Here, we describe a framework for tracking communities over
time in a dynamic network, where a series of significant events is identified
for each community. To this end, a modularity-based strategy is proposed to
effectively detect and track dynamic communities. The potential of our
framework is shown by conducting extensive experiments on synthetic networks
containing embedded events. Results indicate that our framework outperforms
other state-of-the-art methods. In addition, we briefly explore how the
proposed approach can identify dynamic communities in a Twitter network
composed of more than 60,000 users, which posted over 5 million tweets
throughout 2020. The proposed framework can be applied to different social
network and provides a valuable tool to understand the evolution of communities
in dynamic social networks.

[READ FULL TEXT](http://arxiv.org/pdf/2302.12759v1)