Graph Integrated Transformers for Community Detection in Social Networks

TL;DR

This paper introduces GIT-CD, a hybrid model combining GNNs and Transformers to improve community detection in social networks by capturing local and global relationships, with a self-optimizing clustering module.

Contribution

The paper presents a novel Graph Integrated Transformer model that effectively combines GNNs and attention mechanisms for enhanced community detection in social networks.

Findings

GIT-CD outperforms existing models on benchmark datasets.

The hybrid approach captures both local and global network structures.

Self-optimizing clustering improves community assignment accuracy.

Abstract

Community detection is crucial for applications like targeted marketing and recommendation systems. Traditional methods rely on network structure, and embedding-based models integrate semantic information. However, there is a challenge when a model leverages local and global information from complex structures like social networks. Graph Neural Networks (GNNs) and Transformers have shown superior performance in capturing local and global relationships. In this paper, We propose Graph Integrated Transformer for Community Detection (GIT-CD), a hybrid model combining GNNs and Transformer-based attention mechanisms to enhance community detection in social networks. Specifically, the GNN module captures local graph structures, while the Transformer module models long-range dependencies. A self-optimizing clustering module refines community assignments using K-Means, silhouette loss, and KL divergence minimization. Experimental results on benchmark datasets show that GIT-CD outperforms state-of-the-art models, making it a robust approach for detecting meaningful communities in complex social networks.