# Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2104.02153 · HTML: https://mlsanity.com/paper/2104.02153/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Unifying Graph Convolutional Neural Networks and Label Propagation](https://mlsanity.com/paper/2002.06755/) (2020)
- [On the Equivalence of Decoupled Graph Convolution Network and Label Propagation](https://mlsanity.com/paper/2010.12408/) (2020)
- [GuiltyWalker: Distance to illicit nodes in the Bitcoin network](https://mlsanity.com/paper/2102.05373/) (2021)
- [Characterizing and Detecting Money Laundering Activities on the Bitcoin Network](https://mlsanity.com/paper/1912.12060/) (2019)
- [The Unreasonable Effectiveness of Address Clustering](https://mlsanity.com/paper/1605.06369/) (2016)
- [A Graph Convolutional Network Composition Framework for Semi-supervised Classification](https://mlsanity.com/paper/2004.03994/) (2020)

## Built on by (most cited first, 2 of 2)

- [Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering](https://mlsanity.com/paper/2210.10462/) (2022)
- [Neighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks](https://mlsanity.com/paper/2508.00267/) (2025)

## Abstract (arXiv)

We show that a modification of the first layer of a Graph Convolutional Network (GCN) can be used to effectively propagate label information across neighbor nodes, for binary and multi-class classification problems. This is done by selectively eliminating self-loops for the label features during the training phase of a GCN. The GCN architecture is otherwise unchanged, without any extra hyper-parameters, and can be used in both a transductive and inductive setting. We show through several experiments that, depending on how many labels are available during the inference phase, this strategy can lead to a substantial improvement in the model performance compared to a standard GCN approach, including with imbalanced datasets.