mlsanityData to 7 Oct 2026

Cooperative Game Theory Approaches for Network Partitioning

arXiv 1707.03587 ↗First posted 12 Jul 2017cs.SI cs.GT cs.NIcited by 134

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Abstract (arXiv)

The paper is devoted to game-theoretic methods for community detection in networks. The traditional methods for detecting community structure are based on selecting denser subgraphs inside the network. Here we propose to use the methods of cooperative game theory that highlight not only the link density but also the mechanisms of cluster formation. Specifically, we suggest two approaches from cooperative game theory: the first approach is based on the Myerson value, whereas the second approach is based on hedonic games. Both approaches allow to detect clusters with various resolution. However, the tuning of the resolution parameter in the hedonic games approach is particularly intuitive. Furthermore, the modularity based approach and its generalizations can be viewed as particular cases of the hedonic games.

Key facts. Cited by 134 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2017.

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Citing papers per year: 2021: 25; 2022: 25; 2023: 28; 2024: 30; 2025: 19; 2026: 7.

134 papers.

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  1. 2021A Comprehensive Survey on Community Detection with Deep Learningcited by 63
  2. 2021A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learningcited by 55
  3. 2021ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networkscited by 51
  4. 2021Node Dependent Local Smoothing for Scalable Graph Learningcited by 45
  5. 2024LLMs generate structurally realistic social networks but overestimate political homophilycited by 31
  6. 2022A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challengescited by 30
  7. 2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directionscited by 28
  8. 2021Understanding the Dynamics of DNNs Using Graph Modularitycited by 26
  9. 2021Graph Neural Networks: Methods, Applications, and Opportunitiescited by 16
  10. 2021Untrained Graph Neural Networks for Denoisingcited by 15
  11. 2024Spiking Graph Neural Network on Riemannian Manifoldscited by 15
  12. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
38 more
  1. 2023Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Spacecited by 11
  2. 2023An Open-Source Knowledge Graph Ecosystem for the Life Sciencescited by 11
  3. 2024Masked Graph Autoencoder with Non-discrete Bandwidthscited by 11
  4. 2024On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networkscited by 10
  5. 2021Scalable Community Detection via Parallel Correlation Clusteringcited by 9
  6. 2022Principle of Relevant Information for Graph Sparsificationcited by 9
  7. 2022A Unified Framework for Optimization-Based Graph Coarseningcited by 9
  8. 2023Quasi-Monte Carlo Graph Random Featurescited by 9
  9. 2023Hyperbolic Random Forestscited by 9
  10. 2024Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behaviorcited by 9
  11. 2021A Generic Graph Sparsification Framework using Deep Reinforcement Learningcited by 8
  12. 2023CSGCL: Community-Strength-Enhanced Graph Contrastive Learningcited by 7
  13. 2021Disentangling homophily, community structure and triadic closure in networkscited by 6
  14. 2021Spectral clustering under degree heterogeneity: a case for the random walk Laplaciancited by 6
  15. 2023Online Submodular Maximization via Online Convex Optimizationcited by 6
  16. 2023The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structurecited by 6
  17. 2021Topologically Regularized Data Embeddingscited by 5
  18. 2024Network reconstruction via the minimum description length principlecited by 5
  19. 2024Robustness of Decentralised Learning to Nodes and Data Disruptioncited by 5
  20. 2024Refined Graph Encoder Embedding via Self-Training and Latent Community Recoverycited by 5
  21. 2024Exact Certification of (Graph) Neural Networks Against Label Poisoningcited by 5
  22. 2024A Versatile Influence Function for Data Attribution with Non-Decomposable Losscited by 5
  23. 2022Relational representation learning with spike trainscited by 4
  24. 2024Simplifying the Theory on Over-Smoothingcited by 4
  25. 2024Discrete Curvature Graph Information Bottleneckcited by 4
  26. 2022AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generatorscited by 3
  27. 2023Symmetrical SyncMap for Imbalanced General Chunking Problemscited by 3
  28. 2023Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradientscited by 3
  29. 2021Exact Recovery of Community Structures Using DeepWalk and Node2veccited by 2
  30. 2021Mixed membership distribution-free modelcited by 2
  31. 2022A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphscited by 2
  32. 2022Spectral Maps for Learning on Subgraphscited by 2
  33. 2022Hub-aware Random Walk Graph Embedding Methods for Classificationcited by 2
  34. 2022Approximate sampling and estimation of partition functions using neural networkscited by 2
  35. 2022On RKHS Choices for Assessing Graph Generators via Kernel Stein Statisticscited by 2
  36. 2022Bayesian Hyperbolic Multidimensional Scalingcited by 2
  37. 2023A pseudo-likelihood approach to community detection in weighted networkscited by 2
  38. 2023Clustering with Simplicial Complexescited by 2

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