Skip to yearly menu bar Skip to main content


In-Person Poster presentation / top 25% paper

Relational Attention: Generalizing Transformers for Graph-Structured Tasks

Cameron Diao · Ricky Loynd

MH1-2-3-4 #24

Keywords: [ transformers ] [ graph neural networks ] [ Neural Algorithmic Reasoning ] [ graph representation learning ] [ Deep Learning and representational learning ]


Abstract:

Transformers flexibly operate over sets of real-valued vectors representing task-specific entities and their attributes, where each vector might encode one word-piece token and its position in a sequence, or some piece of information that carries no position at all. As set processors, transformers are at a disadvantage in reasoning over more general graph-structured data where nodes represent entities and edges represent relations between entities. To address this shortcoming, we generalize transformer attention to consider and update edge vectors in each transformer layer. We evaluate this relational transformer on a diverse array of graph-structured tasks, including the large and challenging CLRS Algorithmic Reasoning Benchmark. There, it dramatically outperforms state-of-the-art graph neural networks expressly designed to reason over graph-structured data. Our analysis demonstrates that these gains are attributable to relational attention's inherent ability to leverage the greater expressivity of graphs over sets.

Chat is not available.