AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation

• URL: http://arxiv.org/abs/2406.02348v1
• Date: Tue, 4 Jun 2024 14:24:30 GMT
• Title: AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation
• Authors: Peiyu Liang, Hongchang Gao, Xubin He,
• Abstract summary: 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. We propose adaptive modality-wise structure learning (AMoSL) to address these issues.
• Score: 22.84527318463151
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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