Homological Representation Learning for Molecular Graphs
Yoshihiro Maruyama ⋅ Arisa Yasuda
Abstract
We propose Homological Representation Learning (HomRL), an architecture-agnostic regularization method for graph encoders that aligns latent embeddings with an efficiently computable homological signature of the input. In this paper, we give both theoretical results on representation invariance bounds and empirical results on molecular graph classification tasks.
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