Visiting Distant Neighbors in Graph Convolutional Networks
Abstract: We extend the graph convolutional network method for deep learning on graph data to higher order in terms of neighboring nodes. In order to construct representations for a node in a graph, in addition to the features of the node and its immediate neighboring nodes, we also include more distant nodes in the calculations. In experimenting with a number of publicly available citation graph datasets, we show that this higher-order neighbor visiting pays off by outperforming the original model especially when we have a limited number of available labeled data points for the training of the model.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Guillaume_Rabusseau1
Submission Number: 1026
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