City Research Online

Abstract

The analysis of structure in complex datasets has become essential to solving difficult Machine Learning problems. Relational aspects of data, capturing relationships between objects, play a crucial role in understanding the underlying data structure. While traditional graph algorithms have been widely used for binary relations, recent evidence suggests that hypergraphs can provide a more effective approach for modeling complex, non-binary relations. Hypergraph Neural Networks (HGNN) have been shown to offer a small improvement in performance when compared to Graph Neural Networks (GNN). In this paper, a new approach is proposed for inserting relational domain knowledge into HGNNs using a logic clause expressing non-binary relations. We evaluate the performance of this new hypergraph model, called Bottom-clause HGNN (BHGNN), in comparison with well-known approaches. Results show that BHGNN can achieve statistically significant improvement of performance, based on the Wilcoxon signed-ranks test, in comparison with HGNN and GNNs.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords:	Machine learning algorithms, Machine learning, Data structures, Graph neural networks, Logic
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Computer Science
SWORD Depositor:	Symplectic Administrator

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