mlsanityData to 13 Oct 2026

Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching

arXiv 2404.12724 ↗First posted 19 Apr 2024cs.LGcited by 0

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Abstract (arXiv)

In this paper, we propose the Graph-Learning-Dual Graph Convolutional Neural Network called GLDGCN based on the classic Graph Convolutional Neural Network(GCN) by introducing dual convolutional layer and graph learning layer. We apply GLDGCN to the semi-supervised node classification task. Compared with the baseline methods, we achieve higher classification accuracy on three citation networks Citeseer, Cora and Pubmed, and we also analyze and discussabout selection of the hyperparameters and network depth. GLDGCN also perform well on the classic social network KarateClub and the new Wiki-CS dataset. For the insufficient ability of our algorithm to process large graphs during the experiment, we also introduce subgraph clustering and stochastic gradient descent methods into GCN and design a semi-supervised node classification algorithm based on the CLustering Graph Convolutional neural Network, which enables GCN to process large graph and improves its application value. We complete semi-supervised node classification experiments on two classic large graph which are PPI dataset (more than 50,000 nodes) and Reddit dataset (more than 200,000 nodes), and also perform well.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 33 of its 54 references that are in the map. First posted on arXiv 19 Apr 2024.

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33 of 54 references.

References · 33

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21 more
  1. 2020Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networkscited by 257
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  21. 2019Robust Graph Data Learning via Latent Graph Convolutional Representationcited by 1

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