Graph Clustering with Graph Neural Networks

Understand

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 clustering 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.