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Node-Feature Convolution for Graph Convolutional Networks
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DOI：10.1016/j.patcog.2022.108661.png)
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Abstract
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Graph convolutional network (GCN) is an effective neural network model for graph representation learn-ing. However, standard GCN suffers from three main limitations: (1) most real-world graphs have no regular connectivity and node degrees can range from one to hundreds or thousands, (2) neighboring nodes are aggregated with fixed weights, and (3) node features within a node feature vector are con -sidered equally important. Several extensions have been proposed to tackle the limitations respectively. This paper focuses on tackling all the proposed limitations. Specifically, we propose a new node-feature convolutional (NFC) layer for GCN. The NFC layer first constructs a feature map using features selected and ordered from a fixed number of neighbors. It then performs a convolution operation on this feature map to learn the node representation. In this way, we can learn the usefulness of both individual nodes and individual features from a fixed-size neighborhood. Experiments on three benchmark datasets show that NFC-GCN consistently outperforms state-of-the-art methods in node classification. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
Keywords:
Graph
Representation learning
Graph convolutional networks
Convolutional neural networks
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