AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation
<|reference_start|>AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation: 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.<|reference_end|>
@article{liang2024amosl:,
    title={AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph
  Neural Networks For Enhanced Unified Representation},
    author={Peiyu Liang and Hongchang Gao and Xubin He},
    journal={13th International Conference on Soft Computing, Artificial
  Intelligence and Applications (SAI 2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02348},
    primaryClass={cs.LG}
}