mlsanityData to 14 Oct 2026

Community detection in networks: A user guide

arXiv 1608.00163 ↗First posted 30 Jul 2016physics.soc-ph cs.IR cs.SIcited by 105

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Abstract (arXiv)

Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.

Key facts. Cited by 105 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jul 2016.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 24; 2022: 17; 2023: 15; 2024: 20; 2025: 16; 2026: 13.

105 papers.

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  1. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  2. 2021A Comprehensive Survey on Community Detection with Deep Learningcited by 63
  3. 2021Generative hypergraph clustering: from blockmodels to modularitycited by 49
  4. 2023Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?cited by 41
  5. 2022Graph Coloring with Physics-Inspired Graph Neural Networkscited by 29
  6. 2023DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximizationcited by 21
  7. 2022Node-oriented Spectral Filtering for Graph Neural Networkscited by 17
  8. 2024Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approachcited by 17
  9. 2025Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimizationcited by 17
  10. 2022Synthetic Graph Generation to Benchmark Graph Learningcited by 16
  11. 2024Federated Graph Learning with Structure Proxy Alignmentcited by 14
  12. 2022Inference of hyperedges and overlapping communities in hypergraphscited by 13
38 more
  1. 2025QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RLcited by 11
  2. 2024Node Identifiers: Compact, Discrete Representations for Efficient Graph Learningcited by 10
  3. 2022Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernelcited by 8
  4. 2023Normalized mutual information is a biased measure for classification and community detectioncited by 7
  5. 2023Exact Recovery and Bregman Hard Clustering of Node-Attributed Stochastic Block Modelcited by 7
  6. 2021Community Detection in the Stochastic Block Model by Mixed Integer Programmingcited by 5
  7. 2021Retweet communities reveal the main sources of hate speechcited by 5
  8. 2022Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularitycited by 5
  9. 2023Community Detection Using Revised Medoid-Shift Based on KNNcited by 5
  10. 2024Provable Filter for Real-world Graph Clusteringcited by 5
  11. 2021A Survey on Role-Oriented Network Embeddingcited by 4
  12. 2021Community detection using low-dimensional network embedding algorithmscited by 4
  13. 2022Bipartite mixed membership distribution-free model. A novel model for community detection in overlapping bipartite weighted networkscited by 4
  14. 2023Analyzing Modularity Maximization in Approximation, Heuristic, and Graph Neural Network Algorithms for Community Detectioncited by 4
  15. 2025DCA: Graph-Guided Deep Embedding Clustering for Brain Atlasescited by 4
  16. 2021Learning low-rank latent mesoscale structures in networkscited by 3
  17. 2021A useful criterion on studying consistent estimation in community detectioncited by 3
  18. 2021Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truthscited by 3
  19. 2022Implicit models, latent compression, intrinsic biases, and cheap lunches in community detectioncited by 3
  20. 2024Robustness of shallow graph embedding methods for community detectioncited by 3
  21. 2021Hide and Seek: Outwitting Community Detection Algorithmscited by 2
  22. 2021Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Modelcited by 2
  23. 2021Mixed membership distribution-free modelcited by 2
  24. 2022Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorizationcited by 2
  25. 2022Approximate sampling and estimation of partition functions using neural networkscited by 2
  26. 2023Heuristic Modularity Maximization Algorithms for Community Detection Rarely Return an Optimal Partition or Anything Similarcited by 2
  27. 2023Learning distributed representations with efficient SoftMax normalizationcited by 2
  28. 2023Faster Approximation Algorithms for Parameterized Graph Clustering and Edge Labelingcited by 2
  29. 2024Initialisation and Network Effects in Decentralised Federated Learningcited by 2
  30. 2024Longitudinal Modularity, a Modularity for Link Streamscited by 2
  31. 2025The Robustness of Structural Features in Species Interaction Networkscited by 2
  32. 2021Modularity maximisation for graphonscited by 1
  33. 2021TeCoMiner: Topic Discovery Through Term Community Detectioncited by 1
  34. 2021Term-community-based topic detection with variable resolutioncited by 1
  35. 2022Consistency between ordering and clustering methods for graphscited by 1
  36. 2023Graph Neural Network Surrogates of Fair Graph Filteringcited by 1
  37. 2024Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block Modelscited by 1
  38. 2024Redefining Event Types and Group Evolution in Temporal Datacited by 1

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