# Adaptive Graph Auto-Encoder for General Data Clustering

arXiv: https://arxiv.org/abs/2002.08648 · HTML: https://mlsanity.com/paper/2002.08648/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 22 of 22)

- [Hard Sample Aware Network for Contrastive Deep Graph Clustering](https://mlsanity.com/paper/2212.08665/) (2022)
- [Dink-Net: Neural Clustering on Large Graphs](https://mlsanity.com/paper/2305.18405/) (2023)
- [Reinforcement Graph Clustering with Unknown Cluster Number](https://mlsanity.com/paper/2308.06827/) (2023)
- [VTAE: Variational Transformer Autoencoder with Manifolds Learning](https://mlsanity.com/paper/2304.00948/) (2023)
- [Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One](https://mlsanity.com/paper/2304.10126/) (2023)
- [Deep Contrastive Graph Learning with Clustering-Oriented Guidance](https://mlsanity.com/paper/2402.16012/) (2024)
- [CNN2GNN: How to Bridge CNN with GNN](https://mlsanity.com/paper/2404.14822/) (2024)
- [Adaptive Homophily Clustering: Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image](https://mlsanity.com/paper/2501.01595/) (2025)
- [Deep Contrastive Graph Representation via Adaptive Homotopy Learning](https://mlsanity.com/paper/2106.09244/) (2021)
- [Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval](https://mlsanity.com/paper/2206.08842/) (2022)
- [Relating graph auto-encoders to linear models](https://mlsanity.com/paper/2211.01858/) (2022)
- [Deep Embedding Clustering Driven by Sample Stability](https://mlsanity.com/paper/2401.15989/) (2024)
- [Interpretable Multi-View Clustering](https://mlsanity.com/paper/2405.02644/) (2024)
- [textttSynC: Synergistic Boosting of Structure and Representation for Deep Graph Clustering](https://mlsanity.com/paper/2406.15797/) (2024)
- [Learning Disentangled Representations for Generalized Multi-view Clustering](https://mlsanity.com/paper/2605.15640/) (2026)
- [AnchorGAE: General Data Clustering via O(n) Bipartite Graph Convolution](https://mlsanity.com/paper/2111.06586/) (2021)
- [Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasets](https://mlsanity.com/paper/2601.12903/) (2026)
- [Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering](https://mlsanity.com/paper/2603.20829/) (2026)
- [visClust: A visual clustering algorithm based on orthogonal projections](https://mlsanity.com/paper/2211.03894/) (2022)
- [Discretize Relaxed Solution of Spectral Clustering via a Non-Heuristic Algorithm](https://mlsanity.com/paper/2310.12752/) (2023)
- [Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Number](https://mlsanity.com/paper/2407.20119/) (2024)
- [Graph Clustering with Cross-View Feature Propagation](https://mlsanity.com/paper/2408.06029/) (2024)

## Abstract (arXiv)

Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. However, in general clustering tasks, the graph structure of data does not exist such that the strategy to construct a graph is crucial for performance. Therefore, how to extend graph convolution networks into general clustering tasks is an attractive problem. In this paper, we propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs. The adaptive process is designed to induce the model to exploit the high-level information behind data and utilize the non-Euclidean structure sufficiently. We further design a novel mechanism with rigorous analysis to avoid the collapse caused by the adaptive construction. Via combining the generative model for network embedding and graph-based clustering, a graph auto-encoder with a novel decoder is developed such that it performs well in weighted graph used scenarios. Extensive experiments prove the superiority of our model.