论文标题
VGAER：基于图形神经网络重建的社区检测
VGAER: Graph Neural Network Reconstruction based Community Detection
论文作者
论文摘要
社区检测是网络科学中的一个基本问题，但是基于图神经网络的社区检测算法只有少数几个社区检测算法，其中无监督的算法几乎是空白的。通过将高阶模块化信息与网络功能融合在一起，本文首次提出了基于各种图形自动编码器重建的差异图自动编码器重建vgaer，并提供其非稳态版本。他们不需要任何先前的信息。我们根据社区检测任务精心设计了相应的输入功能，解码器和下游任务，这些设计是简洁，自然且性能良好的（我们的设计下的NMI值提高了59.1％-565.9％）。基于一系列具有广泛数据集和高级方法的实验，VGAER取得了出色的性能，并以更简单的设计表现出强大的竞争力和潜力。最后，我们报告了算法收敛分析和T-SNE可视化的结果，这些结果清楚地描述了VGAER的稳定性能和强大的网络模块化能力。我们的代码可在https://github.com/qcydm/vgaer上找到。
Community detection is a fundamental and important issue in network science, but there are only a few community detection algorithms based on graph neural networks, among which unsupervised algorithms are almost blank. By fusing the high-order modularity information with network features, this paper proposes a Variational Graph AutoEncoder Reconstruction based community detection VGAER for the first time, and gives its non-probabilistic version. They do not need any prior information. We have carefully designed corresponding input features, decoder, and downstream tasks based on the community detection task and these designs are concise, natural, and perform well (NMI values under our design are improved by 59.1% - 565.9%). Based on a series of experiments with wide range of datasets and advanced methods, VGAER has achieved superior performance and shows strong competitiveness and potential with a simpler design. Finally, we report the results of algorithm convergence analysis and t-SNE visualization, which clearly depicted the stable performance and powerful network modularity ability of VGAER. Our codes are available at https://github.com/qcydm/VGAER.
