Abstract: Graph neural networks (GNNs) have become the de facto standard for learning over graph-structured data, yet they often falter when labels are scarce, graphs are noisy, or structural patterns ...
Abstract: Graph Convolutional Networks (GCNs) have been widely studied for attribute graph data learning. In many applications, graph node attributes/features may contain various kinds of noises, such ...
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