# Structured Graph Learning for Clustering and Semi-supervised Classification

arXiv: https://arxiv.org/abs/2008.13429 · HTML: https://mlsanity.com/paper/2008.13429/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view](https://mlsanity.com/paper/2102.07943/) (2021)
- [Pseudo-supervised Deep Subspace Clustering](https://mlsanity.com/paper/2104.03531/) (2021)
- [Self-supervised Consensus Representation Learning for Attributed Graph](https://mlsanity.com/paper/2108.04822/) (2021)
- [Sparse Graph Learning Under Laplacian-Related Constraints](https://mlsanity.com/paper/2111.08161/) (2021)
- [Deep Temporal Contrastive Clustering](https://mlsanity.com/paper/2212.14366/) (2022)
- [Learning Sparse Graphs via Majorization-Minimization for Smooth Node Signals](https://mlsanity.com/paper/2202.02815/) (2022)
- [Unsupervised Graph Clustering with Deep Structural Entropy](https://mlsanity.com/paper/2505.14040/) (2025)
- [Smoothed Multi-View Subspace Clustering](https://mlsanity.com/paper/2106.09875/) (2021)
- [Graph-based Semi-supervised Local Clustering with Few Labeled Nodes](https://mlsanity.com/paper/2211.11114/) (2022)
- [DPGNN: Dual-Perception Graph Neural Network for Representation Learning](https://mlsanity.com/paper/2110.07869/) (2021)
- [Fine-grained Graph Learning for Multi-view Subspace Clustering](https://mlsanity.com/paper/2201.04604/) (2022)
- [Graph-based Clustering Revisited: A Relaxation of Kernel k-Means Perspective](https://mlsanity.com/paper/2509.18826/) (2025)
- [Semi-Supervised Graph Learning Meets Dimensionality Reduction](https://mlsanity.com/paper/2203.12522/) (2022)
- [Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering](https://mlsanity.com/paper/2403.18393/) (2024)
- [Multi-Omics Fusion with Soft Labeling for Enhanced Prediction of Distant Metastasis in Nasopharyngeal Carcinoma Patients after Radiotherapy](https://mlsanity.com/paper/2502.09656/) (2025)
- [Learning Conditional Independence Differential Graphs From Time-Dependent Data](https://mlsanity.com/paper/2512.06960/) (2025)

## Abstract (arXiv)

Graphs have become increasingly popular in modeling structures and interactions in a wide variety of problems during the last decade. Graph-based clustering and semi-supervised classification techniques have shown impressive performance. This paper proposes a graph learning framework to preserve both the local and global structure of data. Specifically, our method uses the self-expressiveness of samples to capture the global structure and adaptive neighbor approach to respect the local structure. Furthermore, most existing graph-based methods conduct clustering and semi-supervised classification on the graph learned from the original data matrix, which doesn't have explicit cluster structure, thus they might not achieve the optimal performance. By considering rank constraint, the achieved graph will have exactly c connected components if there are c clusters or classes. As a byproduct of this, graph learning and label inference are jointly and iteratively implemented in a principled way. Theoretically, we show that our model is equivalent to a combination of kernel k-means and k-means methods under certain condition. Extensive experiments on clustering and semi-supervised classification demonstrate that the proposed method outperforms other state-of-the-art methods.