20 years of network community detection
arXiv:2208.00111 · doi:10.1038/s41567-022-01716-7
Abstract
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.
arXiv comment6 pages, 1 figure. Published in Nature Physics
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Cited by in corpus (18)
- Robustness and resilience of complex networks
- The low-rank hypothesis of complex systems
- Local dominance unveils clusters in networks
- Turing pattern theory on homogeneous and heterogeneous higher-order temporal network system
- Heuristic Modularity Maximization Algorithms for Community Detection Rarely Return an Optimal Partition or Anything Similar
- Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection
- Multi-scale Laplacian community detection in heterogeneous networks
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- Smart Walkers in Discrete Space
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- Friendship-paradox paradox: Do most people's friends really have more friends than they do?