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Determinate Node Selection for Semi-supervised Classification Oriented Graph Convolutional Networks

arXiv 2301.04381 ↗First posted 11 Jan 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

Graph Convolutional Networks (GCNs) have been proved successful in the field of semi-supervised node classification by extracting structural information from graph data. However, the random selection of labeled nodes used by GCNs may lead to unstable generalization performance of GCNs. In this paper, we propose an efficient method for the deterministic selection of labeled nodes: the Determinate Node Selection (DNS) algorithm. The DNS algorithm identifies two categories of representative nodes in the graph: typical nodes and divergent nodes. These labeled nodes are selected by exploring the structure of the graph and determining the ability of the nodes to represent the distribution of data within the graph. The DNS algorithm can be applied quite simply on a wide range of semi-supervised graph neural network models for node classification tasks. Through extensive experimentation, we have demonstrated that the incorporation of the DNS algorithm leads to a remarkable improvement in the average accuracy of the model and a significant decrease in the standard deviation, as compared to the original method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 11 Jan 2023.

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14 of 25 references.

References · 14

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
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  10. 2019Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labelscited by 111
  11. 2021Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendationcited by 101
  12. 2019Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matchingcited by 83
2 more
  1. 2021Molecular Contrastive Learning with Chemical Element Knowledge Graphcited by 50
  2. 2022Semi-supervised Learning with Deterministic Labeling and Large Margin Projectioncited by 1

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