用图神经网络增强凝聚算法，无须预设社区数就能更准地发现网络社群。
Hybrid Graph Embeddings and Louvain Algorithm for Unsupervised Community Detection
- 用GNN生成节点嵌入，提升凝聚算法对结构和特征的捕捉能力
- 在真实数据集上动态调整社区数量，准确率显著优于基准方法
- 首个将GNN用于优化凝聚算法的尝试，适合复杂网络分析场景
本文提出一种新型无监督社区发现方法，将凝聚算法与图神经网络（GNN）结合，无需预先知道社区数量即可发现网络中的社群结构。该方法利用GNN生成的节点嵌入，增强凝聚算法对节点间结构与特征信息的感知能力。同时引入合并算法，对增强后的凝聚结果进行优化，有效减少检测出的社区数量。据我们所知，这是首个使用GNN改进凝聚算法进行社区检测的工作。在真实世界数据集上的实验验证了该方法的有效性，结果表明其能动态调整社区数目，并在检测精度上优于现有基准方法。
原文摘要 · Abstract (English)
This paper proposes a novel community detection method that integrates the Louvain algorithm with Graph Neural Networks (GNNs), enabling the discovery of communities without prior knowledge. Compared to most existing solutions, the proposed method does not require prior knowledge of the number of communities. It enhances the Louvain algorithm using node embeddings generated by a GNN to capture richer structural and feature information. Furthermore, it introduces a merging algorithm to refine the results of the enhanced Louvain algorithm, reducing the number of detected communities. To the best of our knowledge, this work is the first one that improves the Louvain algorithm using GNNs for community detection. The improvement of the proposed method was empirically confirmed through an evaluation on real-world datasets. The results demonstrate its ability to dynamically adjust the number of detected communities and increase the detection accuracy in comparison with the benchmark solutions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。