Graph Integrated Transformers for Community Detection in Social Networks
This addresses the problem of detecting meaningful communities in complex social networks for applications like targeted marketing, but it is incremental as it builds on existing GNN and Transformer methods.
The paper tackled the challenge of leveraging local and global information for community detection in social networks by proposing GIT-CD, a hybrid model combining GNNs and Transformers, which outperformed state-of-the-art models on benchmark datasets.
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.