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Most graph neural network architectures work by message-passing node vectorembeddings over the adjacency matrix, and it is assumed that they capture graphtopology by doing that. We design two synthetic tasks, focusing purely ontopological problems -- triangle detection and clique distance -- on whichgraph neural networks perform surprisingly badly, failing to detect those"bermuda" triangles. Datasets and their generation scripts are publiclyavailable on github.com/FujitsuLaboratories/bermudatriangles anddataset.labs.fujitsu.com.

Bermuda Triangles: GNNs Fail to Detect Simple Topological Structures