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