Computer Science > Machine Learning
arXiv:2406.02348 (cs)
[Submitted on 4 Jun 2024]
Title:AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation
View a PDF of the paper titled AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation, by Peiyu Liang and Hongchang Gao and Xubin He
View PDFAbstract:While Multi-view Graph Neural Networks (MVGNNs) excel at leveraging diverse modalities for learning object representation, existing methods assume identical local topology structures across modalities that overlook real-world discrepancies. This leads MVGNNs straggles in modality fusion and representations denoising. To address these issues, we propose adaptive modality-wise structure learning (AMoSL). AMoSL captures node correspondences between modalities via optimal transport, and jointly learning with graph embedding. To enable efficient end-to-end training, we employ an efficient solution for the resulting complex bilevel optimization problem. Furthermore, AMoSL adapts to downstream tasks through unsupervised learning on inter-modality distances. The effectiveness of AMoSL is demonstrated by its ability to train more accurate graph classifiers on six benchmark datasets.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2406.02348 [cs.LG] |
| (orarXiv:2406.02348v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2406.02348 arXiv-issued DOI via DataCite | |
| Journal reference: | 13th International Conference on Soft Computing, Artificial Intelligence and Applications (SAI 2024) |
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View a PDF of the paper titled AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation, by Peiyu Liang and Hongchang Gao and Xubin He
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