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Context-Aware Graph Attention Networks
In particular, CaGAT conducts context-aware learning on both node feature representation and edge (weight) representation simultaneously and cooperatively in a unified manner which can boost their respective performance in network training.
Semi-supervised Learning with Adaptive Neighborhood Graph Propagation Network
In this paper, we first re-interpret graph convolution operation in GCNs as a composition of feature propagation and (non-linear) transformation.
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