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

• URL: http://arxiv.org/abs/2304.10933v1
• Date: Fri, 21 Apr 2023 13:08:53 GMT
• Title: Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers
• Authors: Romain Menegaux and Emmanuel Jehanno and Margot Selosse and Julien Mairal
• Abstract summary: We introduce a novel self-attention mechanism, which we call CSA (Chromatic Self-Attention) We showcase CSA in a fully-attentional graph Transformer CGT (Chromatic Graph Transformer)
• Score: 25.683127388426175
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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