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.

本文研究使用深度模块网络（DMoN）解决无监督图聚类中图分割困难的问题，通过实验表明 DMoN 对于真实世界的数据集能实现与最优结果强相关的高品质聚类效果，其表现优于其他不同指标的汇聚方法。