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Robust graph learning with graph convolutional network
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DOI：10.1016/j.ipm.2022.102916.png)
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Abstract
En 中文
Graph convolutional network (GCN) is a powerful tool to process the graph data and hasachieved satisfactory performance in the task of node classification. In general, GCN uses a fixedgraph to guide the graph convolutional operation. However, the fixed graph from the originalfeature space may contain noises or outliers, which may degrade the effectiveness of GCN. Toaddress this issue, in this paper, we propose a robust graph learning convolutional network(RGLCN). Specifically, we design a robust graph learning model based on the sparse constraintand strong connectivity constraint to achieve the smoothness of the graph learning. In addition,we introduce graph learning model into GCN to explore the representative information, aimingto learning a high-quality graph for the downstream task. Experiments on citation networkdatasets show that the proposed RGLCN outperforms the existing comparison methods withrespect to the task of node classification
Keywords:
Graph convolutional network
Node classification
Sparse constraint
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