A Graph Representation Learning Framework Predicting Potential Multivariate Interactions
Abstract Link prediction is a widely adopted method for extracting valuable data insights from graphs, primarily aimed at predicting interactions between two nodes. However, there are not only pairwise interactions but also multivariate interactions in real life. For example, reactions between multi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-09-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00329-z |
| _version_ | 1827708355128328192 |
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| author | Yanlin Yang Zhonglin Ye Haixing Zhao Lei Meng |
| author_facet | Yanlin Yang Zhonglin Ye Haixing Zhao Lei Meng |
| author_sort | Yanlin Yang |
| collection | DOAJ |
| description | Abstract Link prediction is a widely adopted method for extracting valuable data insights from graphs, primarily aimed at predicting interactions between two nodes. However, there are not only pairwise interactions but also multivariate interactions in real life. For example, reactions between multiple proteins, multiple compounds, and multiple metabolites cannot be mined effectively using link prediction. A hypergraph is a higher-order network composed of nodes and hyperedges, where hyperedges can be composed of multiple nodes, and can be used to depict multivariate interactions. The interactions between multiple nodes can be predicted by hyperlink prediction methods. Since hyperlink prediction requires predicting the interactions between multiple nodes, it makes the study of hyperlink prediction much more complicated than that of other complex networks, thus resulting in relatively limited attention being devoted to this field. The existing hyperlink prediction can only predict potential hyperlinks in uniform hypergraphs, or need to predict hyperlinks based on the candidate hyperlink sets, or only study hyperlink prediction for undirected hypergraphs. Therefore, a hyperlink prediction framework for predicting multivariate interactions based on graph representation learning is proposed to solve the above problems, and then the framework is extended to directed hyperlink prediction (e.g., directed metabolic reaction networks). Furthermore, any size of hyperedges can be predicted by the proposed hyperlink prediction algorithm framework, whose performance is not affected by the number of nodes or the number of hyperedges. Finally, the proposed framework is applied to both the biological metabolic reaction network and the organic chemical reaction network, and experimental analysis has demonstrated that the hyperlinks can be predicted efficiently by the proposed hyperlink prediction framework with relatively low time complexity, and the prediction performance has been improved by up to 40% compared with the baselines. |
| first_indexed | 2024-03-10T17:03:32Z |
| format | Article |
| id | doaj.art-08824b7110d94a4486842c955625b74a |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-10T17:03:32Z |
| publishDate | 2023-09-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-08824b7110d94a4486842c955625b74a2023-11-20T10:52:28ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-09-0116111610.1007/s44196-023-00329-zA Graph Representation Learning Framework Predicting Potential Multivariate InteractionsYanlin Yang0Zhonglin Ye1Haixing Zhao2Lei Meng3College of Computer, Qinghai Normal UniversityCollege of Computer, Qinghai Normal UniversityCollege of Computer, Qinghai Normal UniversityCollege of Computer, Qinghai Normal UniversityAbstract Link prediction is a widely adopted method for extracting valuable data insights from graphs, primarily aimed at predicting interactions between two nodes. However, there are not only pairwise interactions but also multivariate interactions in real life. For example, reactions between multiple proteins, multiple compounds, and multiple metabolites cannot be mined effectively using link prediction. A hypergraph is a higher-order network composed of nodes and hyperedges, where hyperedges can be composed of multiple nodes, and can be used to depict multivariate interactions. The interactions between multiple nodes can be predicted by hyperlink prediction methods. Since hyperlink prediction requires predicting the interactions between multiple nodes, it makes the study of hyperlink prediction much more complicated than that of other complex networks, thus resulting in relatively limited attention being devoted to this field. The existing hyperlink prediction can only predict potential hyperlinks in uniform hypergraphs, or need to predict hyperlinks based on the candidate hyperlink sets, or only study hyperlink prediction for undirected hypergraphs. Therefore, a hyperlink prediction framework for predicting multivariate interactions based on graph representation learning is proposed to solve the above problems, and then the framework is extended to directed hyperlink prediction (e.g., directed metabolic reaction networks). Furthermore, any size of hyperedges can be predicted by the proposed hyperlink prediction algorithm framework, whose performance is not affected by the number of nodes or the number of hyperedges. Finally, the proposed framework is applied to both the biological metabolic reaction network and the organic chemical reaction network, and experimental analysis has demonstrated that the hyperlinks can be predicted efficiently by the proposed hyperlink prediction framework with relatively low time complexity, and the prediction performance has been improved by up to 40% compared with the baselines.https://doi.org/10.1007/s44196-023-00329-zHypergraphHyperlink predictionMultivariate interactionsGraph representation learningBiological metabolic reaction networkOrganic chemical reaction network |
| spellingShingle | Yanlin Yang Zhonglin Ye Haixing Zhao Lei Meng A Graph Representation Learning Framework Predicting Potential Multivariate Interactions International Journal of Computational Intelligence Systems Hypergraph Hyperlink prediction Multivariate interactions Graph representation learning Biological metabolic reaction network Organic chemical reaction network |
| title | A Graph Representation Learning Framework Predicting Potential Multivariate Interactions |
| title_full | A Graph Representation Learning Framework Predicting Potential Multivariate Interactions |
| title_fullStr | A Graph Representation Learning Framework Predicting Potential Multivariate Interactions |
| title_full_unstemmed | A Graph Representation Learning Framework Predicting Potential Multivariate Interactions |
| title_short | A Graph Representation Learning Framework Predicting Potential Multivariate Interactions |
| title_sort | graph representation learning framework predicting potential multivariate interactions |
| topic | Hypergraph Hyperlink prediction Multivariate interactions Graph representation learning Biological metabolic reaction network Organic chemical reaction network |
| url | https://doi.org/10.1007/s44196-023-00329-z |
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