Visiting Distant Neighbors in Graph Convolutional Networks

• URL: http://arxiv.org/abs/2301.10960v3
• Date: Wed, 22 May 2024 19:57:15 GMT
• Title: Visiting Distant Neighbors in Graph Convolutional Networks
• Authors: Alireza Hashemi, Hernan Makse,
• Abstract summary: We extend the graph convolutional network method for deep learning on graph data to higher order in terms of neighboring nodes. We show that this higher order neighbor visiting pays off by outperforming the original model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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