Graph Integrated Transformers for Community Detection in Social Networks

2026-01-07

arXiv.org

AI Summary

This work proposes GIT-CD, a novel model for community detection in social networks that uniquely integrates graph neural networks (GNNs) with Transformer-based attention mechanisms to jointly capture local structural patterns and global long-range dependencies. The model further incorporates a self-optimizing clustering module that dynamically refines community assignments by jointly optimizing K-Means objectives, silhouette coefficient loss, and KL divergence. Extensive experiments on multiple benchmark datasets demonstrate that GIT-CD substantially outperforms current state-of-the-art methods, achieving significant improvements in both accuracy and robustness.

Technology Category

Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunityMachine Learning: Graph-based Machine LearningSearch and Optimization: Combinatorial Optimization

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSocial Networks and Social Media: Social mining and social search on the Web

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.

Problem

• Community Detection
• Social Networks
• Graph Neural Networks
• Transformers
• Local and Global Information

Innovation

• Graph Neural Networks
• Transformer
• Community Detection
• Self-optimizing Clustering
• Long-range Dependencies