Enhancing the Utility of Higher-Order Information in Relational Learning

• URL: http://arxiv.org/abs/2502.09570v1
• Date: Thu, 13 Feb 2025 18:28:17 GMT
• Title: Enhancing the Utility of Higher-Order Information in Relational Learning
• Authors: Raphael Pellegrin, Lukas Fesser, Melanie Weber,
• Abstract summary: We evaluate the effectiveness of hypergraph-level and graph-level architectures in relational learning. We propose hypergraph-level encodings based on classical hypergraph characteristics. Our theoretical analysis shows that hypergraph-level encodings provably increase the representational power of message-passing graph neural networks.
• Score: 0.9899763598214121
• License:
• Abstract: Higher-order information is crucial for relational learning in many domains where relationships extend beyond pairwise interactions. Hypergraphs provide a natural framework for modeling such relationships, which has motivated recent extensions of graph neural net- work architectures to hypergraphs. However, comparisons between hypergraph architectures and standard graph-level models remain limited. In this work, we systematically evaluate a selection of hypergraph-level and graph-level architectures, to determine their effectiveness in leveraging higher-order information in relational learning. Our results show that graph-level architectures applied to hypergraph expansions often outperform hypergraph- level ones, even on inputs that are naturally parametrized as hypergraphs. As an alternative approach for leveraging higher-order information, we propose hypergraph-level encodings based on classical hypergraph characteristics. While these encodings do not significantly improve hypergraph architectures, they yield substantial performance gains when combined with graph-level models. Our theoretical analysis shows that hypergraph-level encodings provably increase the representational power of message-passing graph neural networks beyond that of their graph-level counterparts.

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