Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph
Abstract In recent years, hypergraph neural networks have achieved remarkable success in tasks such as node classification, link prediction, and graph classification, thanks to their powerful computational capabilities. However, most existing hypergraph neural networks are restricted to singlelayer...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-08594-y |
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| author | Libing Bai Feng Hu Chunyang Tang Zhangyu Mei Chuang Liu |
| author_facet | Libing Bai Feng Hu Chunyang Tang Zhangyu Mei Chuang Liu |
| author_sort | Libing Bai |
| collection | DOAJ |
| description | Abstract In recent years, hypergraph neural networks have achieved remarkable success in tasks such as node classification, link prediction, and graph classification, thanks to their powerful computational capabilities. However, most existing hypergraph neural networks are restricted to singlelayer hypergraph, making it challenging to effectively capture the intricate intra-layer higher-order relationships and inter-layer interactions in multilayer hypergraph. Furthermore, these methods typically embed hypergraph features in Euclidean space, which often results in significant distortions when dealing with hypernetworks exhibiting scale-free properties or hierarchical structures. Recently, hyperbolic geometric representation learning has emerged as an effective approach to alleviate such embedding distortions. Building on this foundation, we propose a novel Hyperbolic Multi-channel HyperGraph convolutional Neural Network (HMHGNN). Specifically, a multilayer hypergraph model is first constructed based on singlelayer hypergraphs. Then, a multi-channel convolution mechanism is introduced, which integrates hypergraph’s derivative graph, hypergraph’s line graph, and hyperbolic hypergraph convolution. Subsequently, Euclidean features are mapped to hyperbolic space, and feature transformations are performed within the hyperbolic space. To evaluate the performance of the proposed model, extensive experiments were conducted on three datasets: a scientific collaboration multilayer hypernetwork, a citation multilayer hypernetwork, and a biological multilayer hypernetwork. The experimental results demonstrate that HMHGNN significantly outperforms traditional hypergraph and hyperbolic neural network models in node classification and link prediction tasks. These findings underscore the superior generalization capability and robustness of our model, offering valuable insights into the modeling and analysis of multilayer hypergraph. |
| format | Article |
| id | doaj-art-fac60b0106424e1a813654fec2d3e741 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-fac60b0106424e1a813654fec2d3e7412025-08-20T04:02:55ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-08594-yHyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraphLibing Bai0Feng Hu1Chunyang Tang2Zhangyu Mei3Chuang Liu4Computer College of Qinghai Normal UniversityComputer College of Qinghai Normal UniversityComputer College of Qinghai Normal UniversitySchool of Computer Science, Qufu Normal UniversityAlibaba Research Center for Complexity Sciences, Hangzhou Normal UniversityAbstract In recent years, hypergraph neural networks have achieved remarkable success in tasks such as node classification, link prediction, and graph classification, thanks to their powerful computational capabilities. However, most existing hypergraph neural networks are restricted to singlelayer hypergraph, making it challenging to effectively capture the intricate intra-layer higher-order relationships and inter-layer interactions in multilayer hypergraph. Furthermore, these methods typically embed hypergraph features in Euclidean space, which often results in significant distortions when dealing with hypernetworks exhibiting scale-free properties or hierarchical structures. Recently, hyperbolic geometric representation learning has emerged as an effective approach to alleviate such embedding distortions. Building on this foundation, we propose a novel Hyperbolic Multi-channel HyperGraph convolutional Neural Network (HMHGNN). Specifically, a multilayer hypergraph model is first constructed based on singlelayer hypergraphs. Then, a multi-channel convolution mechanism is introduced, which integrates hypergraph’s derivative graph, hypergraph’s line graph, and hyperbolic hypergraph convolution. Subsequently, Euclidean features are mapped to hyperbolic space, and feature transformations are performed within the hyperbolic space. To evaluate the performance of the proposed model, extensive experiments were conducted on three datasets: a scientific collaboration multilayer hypernetwork, a citation multilayer hypernetwork, and a biological multilayer hypernetwork. The experimental results demonstrate that HMHGNN significantly outperforms traditional hypergraph and hyperbolic neural network models in node classification and link prediction tasks. These findings underscore the superior generalization capability and robustness of our model, offering valuable insights into the modeling and analysis of multilayer hypergraph.https://doi.org/10.1038/s41598-025-08594-y |
| spellingShingle | Libing Bai Feng Hu Chunyang Tang Zhangyu Mei Chuang Liu Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph Scientific Reports |
| title | Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph |
| title_full | Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph |
| title_fullStr | Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph |
| title_full_unstemmed | Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph |
| title_short | Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph |
| title_sort | hyperbolic multi channel hypergraph convolutional neural network based on multilayer hypergraph |
| url | https://doi.org/10.1038/s41598-025-08594-y |
| work_keys_str_mv | AT libingbai hyperbolicmultichannelhypergraphconvolutionalneuralnetworkbasedonmultilayerhypergraph AT fenghu hyperbolicmultichannelhypergraphconvolutionalneuralnetworkbasedonmultilayerhypergraph AT chunyangtang hyperbolicmultichannelhypergraphconvolutionalneuralnetworkbasedonmultilayerhypergraph AT zhangyumei hyperbolicmultichannelhypergraphconvolutionalneuralnetworkbasedonmultilayerhypergraph AT chuangliu hyperbolicmultichannelhypergraphconvolutionalneuralnetworkbasedonmultilayerhypergraph |