# Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification

arXiv: https://arxiv.org/abs/2007.13435 · HTML: https://mlsanity.com/paper/2007.13435/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 11 of 11)

- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [SDGNN: Learning Node Representation for Signed Directed Networks](https://mlsanity.com/paper/2101.02390/) (2021)
- [GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment](https://mlsanity.com/paper/2204.01303/) (2022)
- [Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks](https://mlsanity.com/paper/2301.13492/) (2023)
- [Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network](https://mlsanity.com/paper/2302.00873/) (2023)
- [ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering](https://mlsanity.com/paper/2407.16931/) (2024)
- [GraphPAS: Parallel Architecture Search for Graph Neural Networks](https://mlsanity.com/paper/2112.03461/) (2021)
- [A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2309.01194/) (2023)
- [Semi-Supervised Graph Learning Meets Dimensionality Reduction](https://mlsanity.com/paper/2203.12522/) (2022)
- [Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks](https://mlsanity.com/paper/2205.03753/) (2022)
- [Graph Edge Representation via Tensor Product Graph Convolutional Representation](https://mlsanity.com/paper/2406.14846/) (2024)

## Abstract (arXiv)

Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node. The success of GNNs at node classification depends on the assumption that connected nodes tend to have the same label. However, such an assumption does not always work, limiting the performance of GNNs at node classification. In this paper, we propose label-consistency based graph neural network(LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs. Experiments on benchmark datasets demonstrate the proposed LC-GNN outperforms traditional GNNs in graph-based semi-supervised node classification.We further show the superiority of LC-GNN in sparse scenarios with only a handful of labeled nodes.