融合图神经网络与注意力机制，提升社交网络社区发现效果
Graph Integrated Transformers for Community Detection in Social Networks
- 结合GNN捕捉局部结构与Transformer建模长程依赖
- 在多个基准数据集上优于现有最优模型
- 适合需要精准社区划分的社交分析场景
社区检测在目标营销和推荐系统等应用中至关重要。传统方法依赖网络结构，嵌入模型则整合语义信息，但在复杂社交网络中同时利用局部与全局信息仍具挑战。图神经网络（GNN）和Transformer在捕捉局部与全局关系方面表现优异。本文提出图集成变压器社区检测模型（GIT-CD），融合GNN与基于Transformer的注意力机制，以增强社交网络中的社区检测能力。具体而言，GNN模块捕捉局部图结构，而Transformer模块建模长距离依赖。一个自优化聚类模块通过K-Means、轮廓系数损失和KL散度最小化来优化社区分配。在基准数据集上的实验结果表明，GIT-CD优于当前最优模型，是一种在复杂社交网络中检测有意义社区的稳健方法。
原文摘要 · Abstract (English)
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.
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