Learning to Explain Hypergraph Neural Networks

Sepideh Maleki, Ehsan Hajiramezanali, Gabriele Scalia, Tommaso Biancalani, Kangway V. Chuang

Keywords: hypergraphs, explainability, graph neural network, GNN, HGNN, interpretability

Abstract:

Hypergraphs are expressive structures for describing higher-order relationships among entities, with widespread applications across biology and drug discovery. Hypergraph neural
networks (HGNNs) have recently emerged as a
promising representation learning approach on
these structures for clustering, classification, and
more. However, despite their promising performance, HGNNs remain a black box, and explaining how they make predictions remains an open challenge. To address this problem, we propose
HyperEX, a post-hoc explainability framework
for hypergraphs that can be applied to any trained
HGNN. HyperEX computes node-hyperedge pair
importance to identify sub-hypergraphs as explanations. Our experiments demonstrate how HyperEX learns important sub-hypergraphs responsible for driving node classification to give useful
insight into HGNNs.

Type Of Submission: Extended Abstract (4 pages, non-archival)

Submission Number: 33