Parameterized Hypercomplex Graph Neural Networks for Graph Classification

TL;DR

This paper introduces hypercomplex-valued graph neural networks that learn algebraic multiplication rules from data, leading to improved expressivity, regularization benefits, and state-of-the-art performance with fewer parameters on graph classification tasks.

Contribution

The paper proposes a novel hypercomplex GNN framework that infers algebraic structures during training, enhancing expressivity and efficiency over traditional real-valued GNNs.

Findings

Outperforms real-formulated GNNs with the same capacity.

Reduces memory footprint by 70% or more.

Provides empirical evidence of regularization effects.

Abstract

Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs. Motivated by the complex and quaternion algebras, which have been found in several contexts to enable effective representation learning that inherently incorporates a weight-sharing mechanism, we develop graph neural networks that leverage the properties of hypercomplex feature transformation. In particular, in our proposed class of models, the multiplication rule specifying the algebra itself is inferred from the data during training. Given a fixed model architecture, we present empirical evidence that our proposed model incorporates a regularization effect, alleviating the risk of overfitting. We also show that for fixed model capacity, our proposed method outperforms its corresponding real-formulated GNN, providing additional confirmation for the enhanced expressivity of HC embeddings. Finally, we test our proposed hypercomplex GNN on several open graph benchmark datasets and show that our models reach state-of-the-art performance while consuming a much lower memory footprint with 70& fewer parameters. Our implementations are available at https://github.com/bayer-science-for-a-better-life/phc-gnn.