Adaptive graph convolutional neural network and its biomedical applications
Junzhou Huang and Ruoyu Li, The University of Texas at Arlington, Arlington, TX, United States
Abstract
Graph convolutional neural networks (GCN) are generalizations of classical CNNs to better work with graph-structured data that include biochemical molecular graph, 3D point cloud and social networks. Current convolutional kernels in GCNs were built upon fixed and shared graph structure. However, for most real-world data, graph structure varies in terms of both scale and topology. A generalizable convolutional operator on graph is supposed to be compatible with different graph topologies. In the article, authors introduced a generalized and flexible GCN framework ...
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