# Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

arXiv: https://arxiv.org/abs/2309.00024 · HTML: https://mlsanity.com/paper/2309.00024/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 83 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 30 of 30)

- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Lite Transformer with Long-Short Range Attention](https://mlsanity.com/paper/2004.11886/) (2020)
- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Deep Graph Clustering via Dual Correlation Reduction](https://mlsanity.com/paper/2112.14772/) (2021)
- [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding](https://mlsanity.com/paper/2207.02971/) (2022)
- [Multi-level Feature Learning for Contrastive Multi-view Clustering](https://mlsanity.com/paper/2106.11193/) (2021)
- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [Hard Sample Aware Network for Contrastive Deep Graph Clustering](https://mlsanity.com/paper/2212.08665/) (2022)
- [Cluster-guided Contrastive Graph Clustering Network](https://mlsanity.com/paper/2301.01098/) (2023)
- [Simple Contrastive Graph Clustering](https://mlsanity.com/paper/2205.07865/) (2022)
- [Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion](https://mlsanity.com/paper/2006.08159/) (2020)
- [Ultra-Scalable Spectral Clustering and Ensemble Clustering](https://mlsanity.com/paper/1903.01057/) (2019)
- [Dink-Net: Neural Clustering on Large Graphs](https://mlsanity.com/paper/2305.18405/) (2023)
- [Robust Graph Learning from Noisy Data](https://mlsanity.com/paper/1812.06673/) (2018)
- [Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment](https://mlsanity.com/paper/2303.15689/) (2023)
- [Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view](https://mlsanity.com/paper/2102.07943/) (2021)
- [Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity](https://mlsanity.com/paper/2203.11572/) (2022)
- [Deep Temporal Graph Clustering](https://mlsanity.com/paper/2305.10738/) (2023)
- [Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment](https://mlsanity.com/paper/2302.07491/) (2023)
- [Mixed Graph Contrastive Network for Semi-Supervised Node Classification](https://mlsanity.com/paper/2206.02796/) (2022)
- [Auto-weighted Multi-view Clustering for Large-scale Data](https://mlsanity.com/paper/2303.01983/) (2023)
- [Fast Continual Multi-View Clustering with Incomplete Views](https://mlsanity.com/paper/2306.02389/) (2023)
- [Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph](https://mlsanity.com/paper/2207.02846/) (2022)
- [Multiple Kernel Clustering with Dual Noise Minimization](https://mlsanity.com/paper/2207.06041/) (2022)
- [One-step Multi-view Clustering with Diverse Representation](https://mlsanity.com/paper/2306.05437/) (2023)
- [Market2Dish: Health-aware Food Recommendation](https://mlsanity.com/paper/2012.06416/) (2020)
- [Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning](https://mlsanity.com/paper/2307.03591/) (2023)
- [Multiple Partitions Aligned Clustering](https://mlsanity.com/paper/1909.06008/) (2019)
- [Multiple Graph Learning for Scalable Multi-view Clustering](https://mlsanity.com/paper/2106.15382/) (2021)

## Built on by (most cited first, 5 of 5)

- [Cross-Contrastive Clustering for Multimodal Attributed Graphs with Dual Graph Filtering](https://mlsanity.com/paper/2511.20030/) (2025)
- [Angular Constraint Embedding via SpherePair Loss for Constrained Clustering](https://mlsanity.com/paper/2510.06907/) (2025)
- [LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clustering](https://mlsanity.com/paper/2507.20980/) (2025)
- [Weighted Graph Clustering via Scale Contraction and Graph Structure Learning](https://mlsanity.com/paper/2601.17307/) (2026)
- [Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision](https://mlsanity.com/paper/2608.12027/) (2026)

## Abstract (arXiv)

Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views. Intuitively, a high-quality anchor graph plays an essential role in the success of AMVGC. However, the existing AMVGC methods only consider single-structure information, i.e., local or global structure, which provides insufficient information for the learning task. To be specific, the over-scattered global structure leads to learned anchors failing to depict the cluster partition well. In contrast, the local structure with an improper similarity measure results in potentially inaccurate anchor assignment, ultimately leading to sub-optimal clustering performance. To tackle the issue, we propose a novel anchor-based multi-view graph clustering framework termed Efficient Multi-View Graph Clustering with Local and Global Structure Preservation (EMVGC-LG). Specifically, a unified framework with a theoretical guarantee is designed to capture local and global information. Besides, EMVGC-LG jointly optimizes anchor construction and graph learning to enhance the clustering quality. In addition, EMVGC-LG inherits the linear complexity of existing AMVGC methods respecting the sample number, which is time-economical and scales well with the data size. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method.