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cs.LG2024
Graph Edge Representation via Tensor Product Graph Convolutional Representation
Bo Jiang, Sheng Ge, Ziyan Zhang +3
Graph Convolutional Networks (GCNs) have been widely studied. The core of GCNs is the definition of convolution operators on graphs. However, existing Graph Convolution (GC) operat…
cs.LG2024
Unifying Graph Contrastive Learning via Graph Message Augmentation
Ziyan Zhang, Bo Jiang, Jin Tang +1
Graph contrastive learning is usually performed by first conducting Graph Data Augmentation (GDA) and then employing a contrastive learning pipeline to train GNNs. As we know that…