Graph Inductive Biases in Transformers without Message Passing

• URL: http://arxiv.org/abs/2305.17589v1
• Date: Sat, 27 May 2023 22:26:27 GMT
• Title: Graph Inductive Biases in Transformers without Message Passing
• Authors: Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K. Dokania, Mark Coates, Philip Torr, Ser-Nam Lim
• Abstract summary: New Graph Inductive bias Transformer (GRIT) incorporates graph inductive biases without using message passing. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets.
• Score: 47.238185813842996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more crucial. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) -- a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive -- it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver.

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