Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916286815010816 |
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| author | Wang, Shuai Zhang, David W. Huang, Jia-Hong Rudinac, Stevan Kackovic, Monika Wijnberg, Nachoem Worring, Marcel |
| author_facet | Wang, Shuai Zhang, David W. Huang, Jia-Hong Rudinac, Stevan Kackovic, Monika Wijnberg, Nachoem Worring, Marcel |
| contents | Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural Networks (HGNNs) has emerged as a valuable method to manage the intricate associations in data, though scalability is a notable challenge due to memory limitations. In this study, we introduce a new adaptive sampling strategy specifically designed for hypergraphs, which tackles their unique complexities in an efficient manner. We also present a Random Hyperedge Augmentation (RHA) technique and an additional Multilayer Perceptron (MLP) module to improve the robustness and generalization capabilities of our approach. Thorough experiments with real-world datasets have proven the effectiveness of our method, markedly reducing computational and memory demands while maintaining performance levels akin to conventional HGNNs and other baseline models. This research paves the way for improving both the scalability and efficacy of HGNNs in extensive applications. We will also make our codebase publicly accessible. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13372 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks Wang, Shuai Zhang, David W. Huang, Jia-Hong Rudinac, Stevan Kackovic, Monika Wijnberg, Nachoem Worring, Marcel Machine Learning Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural Networks (HGNNs) has emerged as a valuable method to manage the intricate associations in data, though scalability is a notable challenge due to memory limitations. In this study, we introduce a new adaptive sampling strategy specifically designed for hypergraphs, which tackles their unique complexities in an efficient manner. We also present a Random Hyperedge Augmentation (RHA) technique and an additional Multilayer Perceptron (MLP) module to improve the robustness and generalization capabilities of our approach. Thorough experiments with real-world datasets have proven the effectiveness of our method, markedly reducing computational and memory demands while maintaining performance levels akin to conventional HGNNs and other baseline models. This research paves the way for improving both the scalability and efficacy of HGNNs in extensive applications. We will also make our codebase publicly accessible. |
| title | Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.13372 |