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arXiv · 2312.08671

Permutation-Invariant Graph Partitioning:How Graph Neural Networks Capture Structural Interactions?

Asela Hevapathige·Qing Wang
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Abstract

Graph Neural Networks (GNNs) have paved the way for being a cornerstone in graph-related learning tasks. Yet, the ability of GNNs to capture structural interactions within graphs remains under-explored. In this work, we address this gap by drawing on the insight that permutation invariant graph partitioning enables a powerful way of exploring structural interactions. We establish theoretical connections between permutation invariant graph partitioning and graph isomorphism, and then propose Graph Partitioning Neural Networks (GPNNs), a novel architecture that efficiently enhances the expressive power of GNNs in learning structural interactions. We analyze how partitioning schemes and structural interactions contribute to GNN expressivity and their trade-offs with complexity. Empirically, we demonstrate that GPNNs outperform existing GNN models in capturing structural interactions across diverse graph benchmark tasks.

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Asela Hevapathige, Qing Wang. 2023-12-14. Permutation-Invariant Graph Partitioning:How Graph Neural Networks Capture Structural Interactions?. https://arxiv.org/abs/2312.08671

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