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.

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Towards One-for-All Foundation Model for Attributed Graph Clustering
Oct 6, 2026Yunhui Liu, Xudong Jin, Kang Zhang +5Graph Representation Learning Graph Foundation Models
State Key Laboratory for Novel Software Technology Nanjing University Nanjing, China
Round-Trip KNN Clustering: multiscale hierarchical cluster detection on directed nearest-neighbour graphs
Oct 5, 2026Eraldo Pereira Marinho, Caetano Mazzoni Ranieri, Fabricio Aparecido BreveClustering Hierarchical Clustering
Department of Statistics, Applied Mathematics and Computing, S˜ao Paulo State University (UNESP), Institute of Geosciences and Exact Sciences, Av. 24-A, 1515, Rio Claro, S˜ao Paulo, Brazil.
Neither Black nor White: Balancing Semantic and Collaborative Signals with Graph-Informed Semantic IDs (GrIS)
Oct 1, 2026Aleksei Medvedev, Alejandro Ariza-Casabona, Steven Derby +3Semantic ID Recommendation Graph Clustering
Huawei Ireland Research Centre Dublin, Ireland
Degree-Corrected Joint Matrix Factorization for Multilayer Community Detection
Oct 1, 2026Alexandra Dache, Manon Rustin, Arnaud Vandaele +1Non-Negative Matrix Factorization Graph Clustering
Department of Mathematics and Operational Research, University of Mons, Mons, Belgium
Hierarchical Clustering and Signal Denoising on Digraphs
Sep 28, 2026Yi Wang, Sippanon Kitimoon, Hrushikesh N. Mhaskar +1Graph Signal Processing Hierarchical Clustering
Department of Mathematics, City University of Hong Kong, Hong Kong, SAR China · Data Science Research Center, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand · Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA 91711, USA
Diffusion-Induced Spatial Attention Overlapping Community Detection
Sep 22, 2026Kosti Koistinen, Vesa Kuikka, Joni Herttuainen +3Graph Attention Networks Graph Clustering
Aalto University School of Science Computer Science Department P.O.Box 11000, 00076 AALTO, Finland · Lockheed Martin USA
G-NAC: Graph Neural Automata Clustering via Emergent Domain Formation
Sep 21, 2026Keith Miller, Tristan CrawfordGraph Neural Networks Unsupervised Clustering
When Connected Does Not Mean Similar: Charting the Homophily Boundary of SNAP-KG for Streaming Entity Integration
Sep 14, 2026Jui-Chien Lin, Oshani SeneviratneMulti-View Clustering Graph Clustering
Rensselaer Polytechnic Institute, Troy, NY 12180, USA
Geometric Flow enhanced Graph Coarsening
Sep 14, 2026Chaoqun Fei, Guoxuan Li, Tinglve Zhou +2Graph Neural Networks Graph Coarsening
Selective Hypergraph Refinement for Frozen Graph Clustering
Sep 3, 2026Zimo SiClustering Graph Clustering
Department of Mathematics, Faculty of Science, University of Macau · Macao SAR, China
Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
Sep 1, 2026Hugo Schnoering, Roman Bresson, Michalis VazirgiannisGraph Neural Networks Graph Representation Learning
École Polytechnique, Institut Polytechnique de Paris, France · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
Spectral graph clustering with inhomogeneous latent geometry
Aug 11, 2026Konstantin Avrachenkov, Lucas S. Sibemberg, Alexander Van WerdeSpectral Clustering Graph Clustering
Inria Sophia Antipolis, France · Federal University of Rio Grande do Sul, Brazil · University of Münster, Germany
Density-aware Hierarchical Clustering Based on Element-Categorized Connection Subgraphs
Aug 7, 2026Yuning Yu, José Rodríguez-Piñeiro, Xuefeng Yin +1Clustering Hierarchical Clustering
Tongji University, Shanghai, China · RadioSky (Shanghai) Communication Technology Co., Ltd, Shanghai, China
Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling
Aug 4, 2026Nelson Aloysio Reis de Almeida Passos, Emanuele Carlini, Salvatore TraniGraph Neural Networks Graph Pooling
University of Pisa, Dept. of Computer Science, 56127 Pisa PI, Italy · National Research Council, 56124 Pisa PI, Italy
Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraph
Aug 4, 2026Nelson Aloysio Reis de Almeida Passos, Emanuele Carlini, Salvatore TraniGPU Acceleration Clustering
University of Pisa, Dept. of Computer Science, 56127 Pisa PI, Italy · National Research Council, 56124 Pisa PI, Italy
Interpretable Unsupervised Community Detection with LLM-Symbolized Structured Processes
Aug 2, 2026Aoting Zeng, Kai Wang, Jianwei Wang +3Unsupervised Learning Interpretable ML
Shanghai Jiao Tong University Shanghai, China · University of New South Wales Sydney, Australia
RHEA: Reliability-Harmonized Reconstruction and Assignment for Robust Multimodal-Attributed Graph Clustering
Aug 1, 2026Yinlin Zhu, Di Wu, Ziyu Han +4Multimodal Robustness Clustering
Sun Yat-sen University, Guangzhou, China1 · Shandong University, Weihai, China2 · Beijing Institute of Technology, Beijing, China3
MPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease Classification
Jul 29, 2026Yang Zhang, Xiao Zhou, Jonathan Warrell +3Graph Neural Networks Graph Pooling
A Foundational Perspective for Partitional Clustering on Networks
Jul 27, 2026Derya Ipek Eroglu, Cem IyigunClustering Combinatorial Optimization
Computing Sciences, SUNY Brockport, 350 New Campus Drive, Brockport, 14420, New York, USA · Industrial Engineering, Middle East Technical University, Üniversiteler Mahallesi, Dumlupınar Bulvarı No:1, 06800, Ankara, Turkey
Regularized Optimization on Grassmann Manifold: Theory, Algorithm and Applications
Jul 23, 2026Zhuan Liang, Zheng ZhaiSpectral Regularization Riemannian Optimization
Department of Statistics, Faculty of Arts and Sciences at Beijing Normal University, Zhuhai
DS@GT ARC at AnimalCLEF 2026: Species-Aware Graph Construction for Multi-Species Animal Re-Identification
Jul 17, 2026Evan Sinclair Smith, Anthony Miyaguchi, Snigdha Palamari +1Animal Re-Identification Image Matching
1Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332
Spectral Concentration and Recovery in Sparse High-Dimensional Random Geometric Graphs
Jul 15, 2026Manuel Fernandez, Yizhe ZhuGraph Clustering Spectral Methods
Department of Mathematics, University of Southern California
Hierarchical $\mathcal{F}$ -Clustering: Approximation and Hardness of Clustering into Trees and Bounded Diameter Graphs
Jul 14, 2026Michał Szyfelbein, Dariusz DereniowskiHierarchical Clustering Graph Clustering
Gda´nsk University of Technology
Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
Jul 6, 2026Jingyun Zhang, Hao Peng, Jianxin Li +2Graph Clustering Graph Contrastive Learning
Beihang University Beijing, China · Hangzhou Innovation Institute, Beihang University Hangzhou, China · University of Illinois at Chicago Chicago, USA
CaSPECT: Discovering Causally Homogeneous Subgroups via Directed Spectral Clustering
Jul 3, 2026Arghya Pratihar, Shinjon Chakraborty, Swagatam DasCausal Effect Estimation Causal Structure Learning
1*Electronics and Communication Sciences Unit, Indian Statistical Institute, India. · 2Indian Statistical Institute, India.
Curvature-Guided Sheaf Diffusion for Unsupervised Community Detection on Heterophilic Graphs
Jun 29, 2026Feifan WangGraph Representation Learning Graph Clustering
School of Artificial Intelligence Xiamen Institute of Technology No. 1251, Sunban South Road, Jimei District Xiamen, Fujian 361021, China
Progressive Self-Supervised Learning with Individualized Community Assignment for Brain Network Analysis
Jun 29, 2026Hairui Chen, Yanwu Yang, Jianfeng Cao +3Self-Supervised Learning Graph SSL
Harbin Institute of Technology at Shenzhen, Shenzhen, China · Peng Cheng Laboratory, Shenzhen, China · University of Tübingen, Tübingen, Germany +1
scKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering
Jun 26, 2026Jun Tang, Pengwei Hu, Sicong Gao +3Graph SSL Clustering
College of Computer and Information Science, Southwest University, Chongqing, China · Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
Collapsed Effective Operators for Higher-order Structures
Jun 22, 2026Maximilian Krahn, Lennart Bastian, Vikas Garg +2Graph Clustering Spectral Methods
Department of Computing, Imperial College London, London, UK · Chair of Health Informatics, Technical University of Munich, Germany · Munich Center for Machine Learning, Germany +2
Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning
Jun 22, 2026Xinxi Chen, Junyang Chen, Yiqun Zhang +2Graph Structure Learning Graph Neural Networks
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China

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Towards One-for-All Foundation Model for Attributed Graph Clustering

Yunhui Liu, Xudong Jin, Kang Zhang, Danshuo An+4

State Key Laboratory for Novel Software Technology Nanjing University Nanjing, China

Graph Representation Learning Graph Foundation Models Graph Clustering

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Attributed graph clustering aims to discover node groups by jointly exploiting node attributes and graph topology, yet its unsupervised nature makes model selection and adaptation inherently difficult. Existing methods typically train and tune a separate model for each input graph, leading to costly and fragile pipelines that often fail to transfer across graphs with different feature spaces, structural patterns, and attribute-structure correlations. In this paper, we study a one-for-all alternative: can a single model be trained once and directly applied to diverse attributed graphs without graph-specific training, fine-tuning, or hyperparameter search? We propose OFAG, a foundation model for attributed graph clustering. Building upon Prior-data Fitted Networks, OFAG learns a reusable clustering inference strategy from synthetic attributed graphs generated under broad priors over latent clusters, node attributes, and graph structures. To handle incompatible feature spaces across graphs, OFAG adopts a dimension-agnostic signal-wise graph encoder that treats each feature channel as a graph signal and models its response to shared graph filters. The model is trained with a hyperspherical clustering objective, producing clustering-friendly node representations in a single forward pass at inference time. On ten datasets, one frozen OFAG model achieves the best mean performance and average rank across NMI, ACC, ARI, and F1, while completing all ten datasets in 12.43 minutes total---over 6* faster than the second-fastest baseline and nearly 28* faster than the second-best on clustering quality. Our code and pretrained checkpoint are available at https://github.com/Cloudy1225/OFAG, allowing practitioners to directly apply OFAG to their own attributed graph datasets without additional training or tuning.

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