# Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss

arXiv: https://arxiv.org/abs/2311.12986 · HTML: https://mlsanity.com/paper/2311.12986/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [Attributed Graph Clustering: A Deep Attentional Embedding Approach](https://mlsanity.com/paper/1906.06532/) (2019)
- [Learning Graph Embedding with Adversarial Training Methods](https://mlsanity.com/paper/1901.01250/) (2019)
- [Graph Attention Auto-Encoders](https://mlsanity.com/paper/1905.10715/) (2019)

## Built on by (most cited first, 1 of 1)

- [Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-based Analysis](https://mlsanity.com/paper/2506.02599/) (2025)

## Abstract (arXiv)

Several natural phenomena and complex systems are often represented as networks. Discovering their community structure is a fundamental task for understanding these networks. Many algorithms have been proposed, but recently, Graph Neural Networks (GNN) have emerged as a compelling approach for enhancing this task.In this paper, we introduce a simple, efficient, and clustering-oriented model based on unsupervised Graph Attention AutoEncoder for community detection in attributed networks (GAECO). The proposed model adeptly learns representations from both the network's topology and attribute information, simultaneously addressing dual objectives: reconstruction and community discovery. It places a particular emphasis on discovering compact communities by robustly minimizing clustering errors. The model employs k-means as an objective function and utilizes a multi-head Graph Attention Auto-Encoder for decoding the representations. Experiments conducted on three datasets of attributed networks show that our method surpasses state-of-the-art algorithms in terms of NMI and ARI. Additionally, our approach scales effectively with the size of the network, making it suitable for large-scale applications. The implications of our findings extend beyond biological network interpretation and social network analysis, where knowledge of the fundamental community structure is essential.