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.

通过适应性模态结构学习（AMoSL），我们采用优化传输来捕捉模态之间的节点对应关系，并与图嵌入进行联合学习，从而可以训练更准确的图分类器。