mlsanityData to 7 Oct 2026

20 years of network community detection

arXiv 2208.00111 ↗First posted 30 Jul 2022physics.soc-ph cs.AI cs.SIcited by 23

Not on the map. Why?

Abstract (arXiv)

A fundamental technical challenge in the analysis of network data is the automated discovery of communities - groups of nodes that are strongly connected or that share similar features or roles. In this commentary we review progress in the field over the last 20 years.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 9 of its 40 references that are in the map. First posted on arXiv 30 Jul 2022.

Builds on

9 of 40 references.

References · 9

  1. 2017Inductive Representation Learning on Large Graphscited by 6,284
  2. 2017Graph Embedding Techniques, Applications, and Performance: A Surveycited by 188
  3. 2013Community detection thresholds and the weak Ramanujan propertycited by 47
  4. 2016The ground truth about metadata and community detection in networkscited by 42
  5. 2013Hierarchical Block Structures and High-resolution Model Selection in Large Networkscited by 37
  6. 2015Structure and inference in annotated networkscited by 30
  7. 2014Community detection in networks: Structural communities versus ground truthcited by 14
  8. 2015Generalized communities in networkscited by 4
  9. 2013Structure and dynamics of core-periphery networkscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 5; 2024: 2; 2025: 10; 2026: 2.

23 papers.

Sort:

  1. 2022Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularitycited by 5
  2. 2022Bipartite mixed membership distribution-free model. A novel model for community detection in overlapping bipartite weighted networkscited by 4
  3. 2023Analyzing Modularity Maximization in Approximation, Heuristic, and Graph Neural Network Algorithms for Community Detectioncited by 4
  4. 2022Implicit models, latent compression, intrinsic biases, and cheap lunches in community detectioncited by 3
  5. 2023Heuristic Modularity Maximization Algorithms for Community Detection Rarely Return an Optimal Partition or Anything Similarcited by 2
  6. 2023The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networkscited by 2
  7. 2024Towards Faster Graph Partitioning via Pre-training and Inductive Inferencecited by 2
  8. 2025Efficient Identity and Position Graph Embedding via Spectral-Based Random Feature Aggregationcited by 2
  9. 2025Optimal community detection in dense bipartite graphscited by 1
  10. 2025InfraredGP: Efficient Graph Partitioning via Spectral Graph Neural Networks with Negative Correctionscited by 1
  11. 2022Trading off Quality for Efficiency of Community Detection: An Inductive Method across Graphscited by 0
  12. 2023Community detection in complex networks via node similarity, graph representation learning, and hierarchical clusteringcited by 0
11 more
  1. 2023Superpixels algorithms through network community detectioncited by 0
  2. 2024Multi-order Graph Clustering with Adaptive Node-level Weight Learningcited by 0
  3. 2025Topology of Syntax Networks across Languagescited by 0
  4. 2025Robust Markov stability for community detection at a scale learned based on the structurecited by 0
  5. 2025Recovering Fairness Directly from Modularity: a New Way for Fair Community Partitioningcited by 0
  6. 2025Consistent line clustering using geometric hypergraphscited by 0
  7. 2025Effects of relational graph modularity and depth on the learning performance of neural networkscited by 0
  8. 2025Large Scale Community-Aware Network Generationcited by 0
  9. 2025Alzheimer's Disease Brain Network Miningcited by 0
  10. 2026Graph energy as a measure of community detectability in networkscited by 0
  11. 2026Topology-induced Operators Reveal Complementary Graph Representations without Trainingcited by 0