Understanding Expressivity of GNN in Rule Learning

Haiquan Qiu, Yongqi Zhang, Yong Li, quanming yao

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Keywords: Graph Neural Networks, KG reasoning, Link prediction, Rule learning, Expressivity

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TL;DR: We analyze the expressivity of SOTA GNNs for KG reasoning by deducing the rule strcutures they can learn.

Abstract: Rule learning is critical to improving knowledge graph (KG) reasoning due to their ability to provide logical and interpretable explanations. Recently, Graph Neural Networks (GNNs) with tail entity scoring achieve the state-of-the-art performance on KG reasoning. However, the theoretical understandings for these GNNs are either lacking or focusing on single-relational graphs, leaving what the kind of rules these GNNs can learn an open problem. We propose to fill the above gap in this paper. Specifically, GNNs with tail entity scoring are unified into a common framework. Then, we analyze their expressivity by formally describing the rule structures they can learn and theoretically demonstrating their superiority. These results further inspire us to propose a novel labeling strategy to learn more rules in KG reasoning. Experimental results are consistent with our theoretical findings and verify the effectiveness of our proposed method. The code is publicly available at https://github.com/LARS-research/Rule-learning-expressivity.

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Primary Area: learning on graphs and other geometries & topologies

Submission Number: 6850