# CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning

arXiv: https://arxiv.org/abs/2009.01674 · HTML: https://mlsanity.com/paper/2009.01674/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 18 of 18)

- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive](https://mlsanity.com/paper/2105.07342/) (2021)
- [A Survey on Graph Structure Learning: Progress and Opportunities](https://mlsanity.com/paper/2103.03036/) (2021)
- [SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization](https://mlsanity.com/paper/2303.09778/) (2023)
- [Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach](https://mlsanity.com/paper/2209.08264/) (2022)
- [A Survey of Data-Efficient Graph Learning](https://mlsanity.com/paper/2402.00447/) (2024)
- [MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution](https://mlsanity.com/paper/2208.07012/) (2022)
- [Exploring Correlations of Self-Supervised Tasks for Graphs](https://mlsanity.com/paper/2405.04245/) (2024)
- [Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks](https://mlsanity.com/paper/2106.00717/) (2021)
- [Unsupervised Deep Manifold Attributed Graph Embedding](https://mlsanity.com/paper/2104.13048/) (2021)
- [A unified framework based on graph consensus term for multi-view learning](https://mlsanity.com/paper/2105.11781/) (2021)
- [Variational Graph Contrastive Learning](https://mlsanity.com/paper/2411.07150/) (2024)
- [Locality Relationship Constrained Multi-view Clustering Framework](https://mlsanity.com/paper/2107.05073/) (2021)
- [Graph Representation Learning via Aggregation Enhancement](https://mlsanity.com/paper/2201.12843/) (2022)
- [Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks](https://mlsanity.com/paper/2206.10142/) (2022)
- [Rethinking the positive role of cluster structure in complex networks for link prediction tasks](https://mlsanity.com/paper/2211.02396/) (2022)
- [DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning](https://mlsanity.com/paper/2604.06596/) (2026)
- [MPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease Classification](https://mlsanity.com/paper/2607.28681/) (2026)

## Abstract (arXiv)

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous graph neural networks (GNN) require a large number of labeled nodes, which may not be accessible in real-world graph data. In this paper, we present a novel cluster-aware graph neural network (CAGNN) model for unsupervised graph representation learning using self-supervised techniques. In CAGNN, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments. Moreover, we observe that graphs often contain inter-class edges, which mislead the GNN model to aggregate noisy information from neighborhood nodes. We further refine the graph topology by strengthening intra-class edges and reducing node connections between different classes based on cluster labels, which better preserves cluster structures in the embedding space. We conduct comprehensive experiments on two benchmark tasks using real-world datasets. The results demonstrate the superior performance of the proposed model over existing baseline methods. Notably, our model gains over 7% improvements in terms of accuracy on node clustering over state-of-the-arts.