Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking

• URL: http://arxiv.org/abs/2402.11339v1
• Date: Sat, 17 Feb 2024 17:13:41 GMT
• Title: Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking
• Authors: Simon Zhang, Cheng Xin, Tamal K. Dey
• Abstract summary: Higher-order link prediction is the task of predicting the existence of a missing hyperedge in a hypergraph. A hyperedge representation learned for higher order link prediction is fully expressive when it does not lose distinguishing power up to an isomorphism. We devise a preprocessing algorithm that can identify certain regular subhypergraphs exhibiting symmetry.
• Score: 0.25322020135765466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A hypergraph consists of a set of nodes along with a collection of subsets of the nodes called hyperedges. Higher-order link prediction is the task of predicting the existence of a missing hyperedge in a hypergraph. A hyperedge representation learned for higher order link prediction is fully expressive when it does not lose distinguishing power up to an isomorphism. Many existing hypergraph representation learners, are bounded in expressive power by the Generalized Weisfeiler Lehman-1 (GWL-1) algorithm, a generalization of the Weisfeiler Lehman-1 algorithm. However, GWL-1 has limited expressive power. In fact, induced subhypergraphs with identical GWL-1 valued nodes are indistinguishable. Furthermore, message passing on hypergraphs can already be computationally expensive, especially on GPU memory. To address these limitations, we devise a preprocessing algorithm that can identify certain regular subhypergraphs exhibiting symmetry. Our preprocessing algorithm runs once with complexity the size of the input hypergraph. During training, we randomly replace subhypergraphs identified by the algorithm with covering hyperedges to break symmetry. We show that our method improves the expressivity of GWL-1. Our extensive experiments also demonstrate the effectiveness of our approach for higher-order link prediction on both graph and hypergraph datasets with negligible change in computation.

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