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.
6 pages, 1 figure. Published in Nature Physics
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- Uncovering the overlapping community structure of complex networks in nature and society
- Maps of random walks on complex networks reveal community structure
- Benchmark graphs for testing community detection algorithms
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- Comparing community structure identification
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Cited by in corpus (17)
- 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
- Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularity
- Iterative embedding and reweighting of complex networks reveals community structure
- Quantifying Barriers of Urban Mobility
- The behavior of rich-club coefficient in scale-free networks
- Global decomposition of networks into multiple cores formed by local hubs
- Kaleidoscopic reorganization of network communities across different scales
- Finding community structure using the ordered random graph model
- Smart Walkers in Discrete Space
- Beyond One Solution: The Case for a Comprehensive Exploration of Solution Space in Community Detection
- Friendship-paradox paradox: Do most people's friends really have more friends than they do?