@unpublished{georgiev2022heat,
author = {Georgiev, Dobrik and Brockschmidt, Marc and Allamanis, Miltos},
title = {HEAT: Hyperedge Attention Networks},
year = {2022},
month = {January},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/heat-hyperedge-attention-networks/},
}