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arXiv · 2105.00134

Bermuda Triangles: GNNs Fail to Detect Simple Topological Structures

Arseny Tolmachev·Akira Sakai·Masaru Todoriki·Koji Maruhashi
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Abstract

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.

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Arseny Tolmachev, Akira Sakai, Masaru Todoriki, Koji Maruhashi. 2021-05-01. Bermuda Triangles: GNNs Fail to Detect Simple Topological Structures. https://arxiv.org/abs/2105.00134

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