Bermuda Triangles: GNNs Fail to Detect Simple Topological Structures
This reveals a fundamental limitation in GNNs for graph topology tasks, which could impact applications in network analysis and machine learning.
The paper tackles the problem of graph neural networks (GNNs) failing to detect simple topological structures like triangles and cliques, showing they perform surprisingly badly on synthetic tasks designed for these problems.
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.