Visiting Distant Neighbors in Graph Convolutional Networks
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 28 references that are in the map. First posted on arXiv 26 Jan 2023.
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References · 13
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
- 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
- 2019Strategies for Pre-training Graph Neural Networkscited by 964
- 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
- 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
- 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 442
- 2016Learning Convolutional Neural Networks for Graphscited by 314
- 2015Diffusion-Convolutional Neural Networkscited by 268
- 2021Training Graph Neural Networks with 1000 Layerscited by 169
- 2022Influence-Based Mini-Batching for Graph Neural Networkscited by 15
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