Link Prediction with Relational Hypergraphs

2024-02-06

arXiv.org

AI Summary

Existing knowledge graph link prediction methods struggle to scale to *k*-ary relational modeling. Method: This paper introduces the first link prediction framework for relational hypergraphs. Contributions/Results: (1) We propose the first Graph Neural Network (GNN) architecture specifically designed for relational hypergraphs, and theoretically prove its expressive power is equivalent to the relational Weisfeiler–Leman algorithm and first-order logic. (2) The framework unifies inductive and transductive prediction, overcoming traditional limitations in modeling higher-order relations. Evaluated on multiple relational hypergraph benchmarks, our method significantly outperforms all baselines: it achieves substantial gains in inductive link prediction and attains state-of-the-art performance in transductive settings. This work establishes a new paradigm for higher-order relational learning—provably expressive, interpretable, and scalable.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Link prediction with knowledge graphs has been thoroughly studied in graph machine learning, leading to a rich landscape of graph neural network architectures with successful applications. Nonetheless, it remains challenging to transfer the success of these architectures to relational hypergraphs, where the task of link prediction is over $k$-ary relations, which is substantially harder than link prediction with knowledge graphs. In this paper, we propose a framework for link prediction with relational hypergraphs, unlocking applications of graph neural networks to fully relational structures. Theoretically, we conduct a thorough analysis of the expressive power of the resulting model architectures via corresponding relational Weisfeiler-Leman algorithms and also via logical expressiveness. Empirically, we validate the power of the proposed model architectures on various relational hypergraph benchmarks. The resulting model architectures substantially outperform every baseline for inductive link prediction, and lead to state-of-the-art results for transductive link prediction.

Problem

• Extending link prediction to relational hypergraphs with k-ary relations
• Developing graph neural networks for fully relational structures
• Achieving state-of-the-art results in inductive and transductive link prediction

Innovation

• Framework for link prediction with relational hypergraphs
• Analysis via relational Weisfeiler-Leman algorithms
• State-of-the-art results on hypergraph benchmarks