Community detection algorithms: a comparative analysis: invited presentation, extended abstract
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Summary
Three recent algorithms introduced by Rosvall and Bergstrom and Ronhovde and Nussinov have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.
- Type
- article
- Published
- 2009-08-07
- Cited by
- 2,281
- References
- 77
- Access
- Open access
- OpenAlex
- https://openalex.org/W1995996823
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14193110
Keywords
Benchmark (surveying), Community structure, Computer science, Algorithm, Class (philosophy)
References
- IL-13受体α2降低血吸虫病肉芽肿的炎症反应并延长宿主存活时间[英]/Mentink-Kane MM,Cheever AW，Thompson RW，et al//Proc Natl Acad Sci U S A
- Large Scale Structure and Dynamics of Complex Networks: From Information Technology to Finance and Natural Science
- Encyclopedia of Complexity and Systems Science
- Community structure in social and biological networks
- Multiresolution community detection for megascale networks by information-based replica correlations.
- Detection of community structures in networks via global optimization
- Partitioning and modularity of graphs with arbitrary degree distribution.
- Inversion method for content-based networks.
- Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities.
- Detection of node group membership in networks with group overlap
- Functional cartography of complex metabolic networks
- Benchmark graphs for testing community detection algorithms.
- Algorithms for graph partitioning on the planted partition model
- Statistical mechanics of community detection.
- Defining and identifying communities in networks.
- Line graphs, link partitions, and overlapping communities.
- A Critical Point for Random Graphs with a Given Degree Sequence
- Finding community structure in very large networks.
- Subgraphs in random networks.
- Modeling By Shortest Data Description*
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- Ordered and continuous community structure in networks
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