Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networks

TL;DR

This paper introduces a reinforcement learning-based method for community detection in dynamic social networks, focusing on local modularity optimization to efficiently handle evolving and large-scale networks.

Contribution

It presents a novel reinforcement learning approach for dynamic community detection that adapts to network changes using local modularity optimization.

Findings

Results are comparable to static methods on synthetic data.

Effective in handling large, evolving networks.

Demonstrates adaptability to dynamic network changes.

Abstract

The identification of community structure in a social network is an important problem tackled in the literature of network analysis. There are many solutions to this problem using a static scenario, when facing a dynamic scenario some solutions may be adapted but others simply do not fit, moreover when considering the demand to analyze constantly growing networks. In this context, we propose an approach to the problem of community detection in dynamic networks based on a reinforcement learning strategy to deal with changes on big networks using a local optimization on the modularity score of the changed entities. An experiment using synthetic and real-world dynamic network data shows results comparable to static scenarios.