I2HGNN: Iterative Interpretable HyperGraph Neural Network for semi-supervised classification.

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hongwei Zhang, Saizhuo Wang, Zixin Hu, Yuan Qi, Zengfeng Huang, Jian Guo
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引用次数: 0

Abstract

Learning on hypergraphs has garnered significant attention recently due to their ability to effectively represent complex higher-order interactions among multiple entities compared to conventional graphs. Nevertheless, the majority of existing methods are direct extensions of graph neural networks, and they exhibit noteworthy limitations. Specifically, most of these approaches primarily rely on either the Laplacian matrix with information distortion or heuristic message passing techniques. The former tends to escalate algorithmic complexity, while the latter lacks a solid theoretical foundation. To address these limitations, we propose a novel hypergraph neural network named I2HGNN, which is grounded in an energy minimization function formulated for hypergraphs. Our analysis reveals that propagation layers align well with the message-passing paradigm in the context of hypergraphs. I2HGNN achieves a favorable trade-off between performance and interpretability. Furthermore, it effectively balances the significance of node features and hypergraph topology across a diverse range of datasets. We conducted extensive experiments on 15 datasets, and the results highlight the superior performance of I2HGNN in the task of hypergraph node classification across nearly all benchmarking datasets.

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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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