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GRAPH CONVOLUTIONAL NETWORKS WITH MOTIF-BASED ATTENTION
GRAPH CONVOLUTIONAL NETWORKS WITH MOTIF-BASED ATTENTION
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机译:具有基于MOTIF的注意力的图形卷积网络
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摘要
Various embodiments describe techniques for making inferences from graph-structured data using graph convolutional networks (GCNs). The GCNs use various pre-defined motifs to filter and select adjacent nodes for graph convolution at individual nodes, rather than merely using edge-defined immediate-neighbor adjacency for information integration at each node. In certain embodiments, the graph convolutional networks use attention mechanisms to select a motif from multiple motifs and select a step size for each respective node in a graph, in order to capture information from the most relevant neighborhood of the respective node.
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