Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Vērsta kopienu noteikšana× | Sociālo tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2008 | 1934 (sociometry); 1994 (modern formalization) |
| Autors≠ | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. | Moreno, J.L.; formalized by Wasserman & Faust |
| Tips≠ | Graph partitioning / modularity optimization | Structural/relational analysis framework |
| Pirmavots≠ | Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Citi nosaukumi | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning | SNA, network analysis, sociometric analysis, relational analysis |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Directed community detection identifies densely interconnected groups of nodes in a directed network, accounting for the asymmetry of edges (e.g., A follows B does not imply B follows A). Adapting modularity or flow-based criteria to directed graphs reveals clusters that undirected methods systematically miss, making it essential for citation networks, follower graphs, and biological regulatory pathways. | Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system. |
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