Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Detecția dirijată a comunităților× | Analiza Rețelelor Sociale× | |
|---|---|---|
| Domeniu | Analiza rețelelor | Analiza rețelelor |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2008 | 1934 (sociometry); 1994 (modern formalization) |
| Autorul original≠ | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. | Moreno, J.L.; formalized by Wasserman & Faust |
| Tip≠ | Graph partitioning / modularity optimization | Structural/relational analysis framework |
| Sursa seminală≠ | 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 |
| Denumiri alternative | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning | SNA, network analysis, sociometric analysis, relational analysis |
| Înrudite≠ | 6 | 5 |
| Rezumat≠ | 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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