211 citations · 559 across the 29 of their papers we have counts for
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cs.LG2019★ 6 cited
Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural Networks
Jiaxiang Tang, Wei Hu, Xiang Gao +1
Graph Convolutional Neural Networks (GCNNs) extend classical CNNs to graph data domain, such as brain networks, social networks and 3D point clouds. It is critical to identify an a…
cs.LG2019★ 4 cited
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification
Xiang Gao, Wei Hu, Zongming Guo
Graph Convolutional Neural Networks (GCNNs) are generalizations of CNNs to graph-structured data, in which convolution is guided by the graph topology. In many cases where graphs a…