# EGRC-Net: Embedding-induced Graph Refinement Clustering Network

arXiv: https://arxiv.org/abs/2211.10627 · HTML: https://mlsanity.com/paper/2211.10627/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 64 references that are in the map.
- First posted on arXiv 19 Nov 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Understanding over-squashing and bottlenecks on graphs via curvature](https://mlsanity.com/paper/2111.14522/) (2021)
- [Scaling Graph Neural Networks with Approximate PageRank](https://mlsanity.com/paper/2007.01570/) (2020)
- [Attributed Graph Clustering: A Deep Attentional Embedding Approach](https://mlsanity.com/paper/1906.06532/) (2019)
- [Structural Deep Clustering Network](https://mlsanity.com/paper/2002.01633/) (2020)
- [SPICE: Semantic Pseudo-labeling for Image Clustering](https://mlsanity.com/paper/2103.09382/) (2021)
- [Learning Graph Embedding with Adversarial Training Methods](https://mlsanity.com/paper/1901.01250/) (2019)
- [Multi-view Contrastive Graph Clustering](https://mlsanity.com/paper/2110.11842/) (2021)
- [Attention-driven Graph Clustering Network](https://mlsanity.com/paper/2108.05499/) (2021)
- [Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering](https://mlsanity.com/paper/2107.08562/) (2021)
- [Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study](https://mlsanity.com/paper/2108.10521/) (2021)
- [Adaptive Propagation Graph Convolutional Network](https://mlsanity.com/paper/2002.10306/) (2020)
- [Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction](https://mlsanity.com/paper/2202.00961/) (2022)
- [CGC: Contrastive Graph Clustering for Community Detection and Tracking](https://mlsanity.com/paper/2204.08504/) (2022)
- [SAIL: Self-Augmented Graph Contrastive Learning](https://mlsanity.com/paper/2009.00934/) (2020)
- [Deep Attention-guided Graph Clustering with Dual Self-supervision](https://mlsanity.com/paper/2111.05548/) (2021)
- [Maximum Entropy Subspace Clustering Network](https://mlsanity.com/paper/2012.03176/) (2020)
- [Objective-Based Hierarchical Clustering of Deep Embedding Vectors](https://mlsanity.com/paper/2012.08466/) (2020)
- [Adaptive Attribute and Structure Subspace Clustering Network](https://mlsanity.com/paper/2109.13742/) (2021)
- [Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation](https://mlsanity.com/paper/2205.10481/) (2022)
- [Decorrelated Clustering with Data Selection Bias](https://mlsanity.com/paper/2006.15874/) (2020)
- [Clustering Ensemble Meets Low-rank Tensor Approximation](https://mlsanity.com/paper/2012.08916/) (2020)
- [Personalized PageRank Graph Attention Networks](https://mlsanity.com/paper/2205.14259/) (2022)
- [Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning](https://mlsanity.com/paper/2110.04683/) (2021)

## Built on by (most cited first, 3 of 3)

- [textttSynC: Synergistic Boosting of Structure and Representation for Deep Graph Clustering](https://mlsanity.com/paper/2406.15797/) (2024)
- [Generalization Performance of Ensemble Clustering: From Theory to Algorithm](https://mlsanity.com/paper/2506.02053/) (2025)
- [Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering](https://mlsanity.com/paper/2506.09920/) (2025)

## Abstract (arXiv)

Existing graph clustering networks heavily rely on a predefined yet fixed graph, which can lead to failures when the initial graph fails to accurately capture the data topology structure of the embedding space. In order to address this issue, we propose a novel clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net), which effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. To begin, we leverage both semantic and topological information by employing a vanilla auto-encoder and a graph convolution network, respectively, to learn a latent feature representation. Subsequently, we utilize the local geometric structure within the feature embedding space to construct an adjacency matrix for the graph. This adjacency matrix is dynamically fused with the initial one using our proposed fusion architecture. To train the network in an unsupervised manner, we minimize the Jeffreys divergence between multiple derived distributions. Additionally, we introduce an improved approximate personalized propagation of neural predictions to replace the standard graph convolution network, enabling EGRC-Net to scale effectively. Through extensive experiments conducted on nine widely-used benchmark datasets, we demonstrate that our proposed methods consistently outperform several state-of-the-art approaches. Notably, EGRC-Net achieves an improvement of more than 11.99% in Adjusted Rand Index (ARI) over the best baseline on the DBLP dataset. Furthermore, our scalable approach exhibits a 10.73% gain in ARI while reducing memory usage by 33.73% and decreasing running time by 19.71%. The code for EGRC-Net will be made publicly available at urlhttps://github.com/ZhihaoPENG-CityU/EGRC-Net.