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|a 10.1109/TPAMI.2025.3585179
|2 doi
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|a eng
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| 100 |
1 |
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|a Feng, Yifan
|e verfasserin
|4 aut
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| 245 |
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|a Kernelized Hypergraph Neural Networks
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|c 2025
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|a Date Revised 12.09.2025
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a Hypergraph Neural Networks (HGNNs) have attracted much attention for high-order structural data learning. Existing methods mainly focus on simple mean-based aggregation or manually combining multiple aggregations to capture multiple information on hypergraphs. However, those methods inherently lack continuous non-linear modeling ability and are sensitive to varied distributions. Although some kernel-based aggregations on GNNs and CNNs can capture non-linear patterns to some degree, those methods are restricted in the low-order correlation and may cause unstable computation in training. In this work, we introduce Kernelized Hypergraph Neural Networks (KHGNN) and its variant, Half-Kernelized Hypergraph Neural Networks (H-KHGNN), which synergize mean-based and max-based aggregation functions to enhance representation learning on hypergraphs. KHGNN's kernelized aggregation strategy adaptively captures both semantic and structural information via learnable parameters, offering a mathematically grounded blend of kernelized aggregation approaches for comprehensive feature extraction. H-KHGNN addresses the challenge of overfitting in less intricate hypergraphs by employing non-linear aggregation selectively in the vertex-to-hyperedge message-passing process, thus reducing model complexity. Our theoretical contributions reveal a bounded gradient for kernelized aggregation, ensuring stability during training and inference. Empirical results demonstrate that KHGNN and H-KHGNN outperform state-of-the-art models across 10 graph/hypergraph datasets, with ablation studies demonstrating the effectiveness and computational stability of our method
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|a Journal Article
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| 700 |
1 |
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|a Zhang, Yifan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ying, Shihui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Du, Shaoyi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gao, Yue
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 47(2025), 10 vom: 01. Sept., Seite 8938-8954
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
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| 773 |
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|g volume:47
|g year:2025
|g number:10
|g day:01
|g month:09
|g pages:8938-8954
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2025.3585179
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