HEAT: Hyperedge Attention Networks

• URL: http://arxiv.org/abs/2201.12113v1
• Date: Fri, 28 Jan 2022 13:42:01 GMT
• Title: HEAT: Hyperedge Attention Networks
• Authors: Dobrik Georgiev, Marc Brockschmidt, Miltiadis Allamanis
• Abstract summary: HEAT is a neural model capable of representing typed and qualified hypergraphs. It can be viewed as a generalization of both message passing neural networks and Transformers. We evaluate it on knowledge base completion and on bug detection and repair using a novel hypergraph representation of programs.
• Score: 34.65832569321654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning from structured data is a core machine learning task. Commonly, such data is represented as graphs, which normally only consider (typed) binary relationships between pairs of nodes. This is a substantial limitation for many domains with highly-structured data. One important such domain is source code, where hypergraph-based representations can better capture the semantically rich and structured nature of code. In this work, we present HEAT, a neural model capable of representing typed and qualified hypergraphs, where each hyperedge explicitly qualifies how participating nodes contribute. It can be viewed as a generalization of both message passing neural networks and Transformers. We evaluate HEAT on knowledge base completion and on bug detection and repair using a novel hypergraph representation of programs. In both settings, it outperforms strong baselines, indicating its power and generality.

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