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Journal Articles Transactions on Machine Learning Research Journal Year : 2023

Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers

Abstract

We introduce a novel self-attention mechanism, which we call CSA (Chromatic Self-Attention), which extends the notion of attention scores to attention _filters_, independently modulating the feature channels. We showcase CSA in a fully-attentional graph Transformer CGT (Chromatic Graph Transformer) which integrates both graph structural information and edge features, completely bypassing the need for local message-passing components. Our method flexibly encodes graph structure through node-node interactions, by enriching the original edge features with a relative positional encoding scheme. We propose a new scheme based on random walks that encodes both structural and positional information, and show how to incorporate higher-order topological information, such as rings in molecular graphs. Our approach achieves state-of-the-art results on the ZINC benchmark dataset, while providing a flexible framework for encoding graph structure and incorporating higher-order topology.
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Dates and versions

hal-04105101 , version 1 (24-05-2023)
hal-04105101 , version 2 (09-01-2024)

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Romain Menegaux, Emmanuel Jehanno, Margot Selosse, Julien Mairal. Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers. Transactions on Machine Learning Research Journal, 2023. ⟨hal-04105101v2⟩
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