Graph Clustering

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7 papers in the last four weeks, up 133% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 53

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scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering
Jun 17, 2026Jinke Wu, Yifan Wang, Siyu Yi +5Graph SSL Clustering
Sichuan University · University of International Business and Economics · Great Bay University +1
Clustering Node Attributed Networks with Graph Neural Networks and Self Learning
Jun 11, 2026Rodrigo de Sapienza Luna, Daniel Ratton FigueiredoGraph Neural Networks Graph Representation Learning
Systems Engineering and Computer Science (PESC), Federal University of Rio de Janeiro (UFRJ), Technology Center, Block H, Room 319, 68511, Cidade Universitária, Rio de Janeiro, Brazil
Recovering the Zipfian Distribution in Unsupervised Term Discovery
Jun 9, 2026Danel Slabbert, Simon Malan, Herman KamperClustering Hierarchical Clustering
Electrical and Electronic Engineering, Stellenbosch University, South Africa
Thresholded Local Hyper-Flow Diffusion
Jun 8, 2026Meher Chaitanya, Sebastian Dalleiger, Luana RuizSubmodular Optimization Graph Clustering
KTH Royal Institute of Technology · Stockholm, Sweden 114 28 · Johns Hopkins University +1
Alcmean's: Unsupervised community detection using local Laplacian, automatic detection of the number of centers
Jun 8, 2026Shahin Momenzadeh, Rojiar Pir MohammadianiClustering Graph Clustering
Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
On solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem
Jun 6, 2026Rok Hribar, Gregor Papa, Janez Povh +1Non-Negative Matrix Factorization Graph Clustering
Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, Ljubljana, 1000, Slovenia. · Laboratory for Engineering Design, Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva cesta 6, Ljubljana, 1000, Slovenia. · Rudolfovo – Science and Technology Centre Novo mesto, Podbreznik 15, Novo mesto, 8000, Slovenia. +1
Sharp Low-Degree Thresholds for Planted-vs-Planted Testing
Jun 3, 2026Anda Skeja, Daniel Gutiérrez Espinoza, Fiona Skerman +1Two-Sample Testing Graph Clustering
Department of Mathematics, Uppsala University · Department of Mathematics, University of California, Davis
A Nonmonotone Gradient-Based Algorithm for Symmetric Nonnegative Matrix Factorization and Graph Clustering
Jun 1, 2026Ryan Swart, Johannes BrustNon-Negative Matrix Factorization Graph Clustering
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ
Query-Limited Community Recovery in Stochastic Block Models
Jun 1, 2026Sabyasachi Basu, Manuj Mukherjee, Lutz Oettershagen +1Active Learning Adaptive Sampling
1Microsoft Research, India · 2Indraprastha Institute of Information Technology, India · University of Liverpool, United Kingdom +1
Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models
May 27, 2026Simon Queric, Cédric Vincent-Cuaz, Charles Bouveyron +1Model Selection Latent Variable Models
Universit´e Cˆote d’Azur, Inria, CNRS LJAD, Maasai Nice, France · EPFL Lausanne, Switzerland · Universit´e Cˆote d’Azur, Inria, CNRS CEPAM, Maasai Nice, France
Robust Contrastive Graph Clustering with Adaptive Local-Global Integration
May 27, 2026Lei Zhang, Fubo Sun, Haipeng Yang +2Graph Representation Learning Multi-Scale Feature Fusion
School of Computer Science and Technology, Anhui University · College of Management and Economics, Tianjin University
Neural Acceleration for Graph Partitioning
May 18, 2026Joshua Dennis Booth, Vishvam PatelGraph Clustering Spectral Methods
Department of Computer Science University of Alabama in Huntsville Huntsville AL, USA
Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection
May 18, 2026Jiatong XieGraph Clustering Spectral Methods
Independent researcher.
Reconnecting Fragmented Citation Networks with Semantic Augmentation
May 12, 2026Vu Thi Huong, Annika Buchholz, Imene Khebouri +5Graph Structure Learning Graph Clustering
Digital Data and Information for Society, Science, and Culture, Zuse Institute Berlin, Takustr. 7, 14195 Berlin, Germany · Institute of Mathematics, Vietnam Academy of Science and Technology, 10072 Hanoi, Vietnam · Software and Algorithms for Discrete Optimization, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany +2
AdaGraph: A Graph-Native Clustering Algorithm That Overcomes the Curse of Dimensionality and Enables Scientific Discovery
May 5, 2026Ahmed ElmahdiClustering Graph Clustering
Independent Researcher
Simplify to Amplify: Achieving Information-Theoretic Bounds with Fewer Steps in Spectral Community Detection
Feb 19, 2026Sie Hendrata Dharmawan, Peter ChinStochastic Block Model Graph Clustering
Thayer School Of Engineering · Dartmouth College · Hanover, NH, USA
Incremental (k, z)-Clustering on Graphs
Feb 9, 2026Emilio Cruciani, Sebastian Forster, Antonis SkarlatosClustering Graph Clustering
European University of Rome, Italy. · Department of Computer Science, University of Salzburg, Salzburg, Austria. · Department of Computer Science, University of Warwick, Coventry, England.
Double-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clusterin
Jan 30, 2025Fangyuan Xie, Jinghui Yuan, Feiping Nie +1Graph Clustering Optimal Transport
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, P.R. China
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping
Aug 6, 2024Jinghui Yuan, Chusheng Zeng, Fangyuan Xie +5Clustering Graph Clustering
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China · Institute of Artificial Intelligence (TeleAI), China Telecom, P. R. China
Robustness of shallow graph embedding methods for community detection
May 1, 2024Zhi-Feng Wei, Pablo Moriano, Ramakrishnan KannanGraph Clustering Graph Embedding
Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, Richland, 99354, WA, USA. · Department of Mathematics, Indiana University, Bloomington, 47405, IN, USA. · Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, 37830, TN, USA.
Provable Filter for Real-world Graph Clustering
Mar 6, 2024Xuanting Xie, Erlin Pan, Zhao Kang +2Graph Clustering Graph Heterophily
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China · Alibaba Group · Michigan State University
Semidefinite programming relaxations and debiasing for MAXCUT-based clustering
Jan 16, 2024Shuheng ZhouSemidefinite Programming Unsupervised Clustering
University of California, Riverside, CA 92521
Parametrized Power-Iteration Clustering for Directed Graphs
Oct 1, 2022Gwendal Debaussart-Joniec, Harry Sevi, Matthieu Jonckheere +1Graph Clustering Graph Embedding
Universit´e Paris-Saclay, ENS Paris-Saclay, Centre Borelli, CNRS, France · CNRS, LAAS, France

Jun 17, 2026, cs.LGJ/K move · Enter open · S save

scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering

Jinke Wu, Yifan Wang, Siyu Yi, Caiyang Yu+4

Sichuan University · University of International Business and Economics · Great Bay University+1

Graph SSL Clustering Graph Clustering Graph Transformer Single-Cell RNA Sequencing

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Single-cell RNA sequencing (scRNA-seq) serves a pivotal role in characterizing gene expression at the cellular level, enabling the identification of cell types and advancing the understanding of cellular heterogeneity. Despite the significant progress in scRNA-seq data clustering, we argue that current methods always ignore the sparsity and noise, as well as the complex intercellular structural information inherent in scRNA-seq data. Toward this end, in this paper, we propose a novel single-cell RNA-seq clustering framework via deep Siamese Graph Transformer Network (termed scGTN), which explicitly integrates gene expression profile and intercellular structural dependencies for cell clustering. In particular, we formulate scRNA-seq data as a graph and construct two augmented graph views that serve as dual views to capture complementary intercellular information. Then, a Siamese graph transformer network is employed to explicitly incorporate shortest-path information and node-wise distances for capturing richer structural relationships between cells. Finally, we employ an optimal transport strategy to guide the cell clustering in a self-supervised manner. Extensive experiments on multiple benchmark scRNA-seq datasets demonstrate that our scGTN consistently outperforms existing methods. Our code is available at https://github.com/W-RMSL/scGTN.

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