Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs. Graph clustering has the same overall goal as node pooling in GNNs - does this mean that GNN pooling methods do a good job at clusterings graphs? Surprisingly, the answer is no - current GNN pooling methods often fail to recover the cluster structure in cases where simple baselines, such as k-means applied on learned representations, work well. We investigate further by carefully designing a set of experiments to study different signal-to-noise scenarios both in graph structure and attribute data. To address these methods' poor performance in clustering, we introduce Deep Modularity Networks (DMoN), an unsupervised pooling method inspired by the modularity measure of clustering quality, and show how it tackles recovery of the challenging clustering structure of real-world graphs. Similarly, on real-world data, we show that DMoN produces high quality clusters which correlate strongly with ground truth labels, achieving state-of-the-art results with over 40% improvement over other pooling methods across different metrics.

翻译：神经网图(GNNs)在许多图表分析任务(如节点分类和链接预测)上取得了最新的结果。然而,在图表(如图形群集)上,一些重要的未受监督的问题(如图形群集)比GNNS的进展更加难以应对。 图形群集的总体目标与GNNs的节点集合目标相同吗?这是否意味着GNN集合方法在组合图中做得很好? 令人惊讶的是,答案是否定的 - 目前的GNN集合方法往往无法在简单基线(如K手段在学习的演示中应用K- means,工作良好)中恢复集群结构。我们通过仔细设计一套实验来进一步调查,以研究图形结构和属性数据中不同的信号到噪音情景。为了解决这些方法在组合中的不良性能,我们引入了深模块网络(DMN),这是受组合质量测量质量衡量标准激励的、不受监督的集合方法,并显示它如何应对现实世界图中具有挑战的组合结构结构的恢复。同样,我们在现实世界数据中显示DMoN将高质量分组与其他40个基点联系起来。