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| Насочена анализ на социални мрежи× | Насочена детекция на общности× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 1994 | 2008 |
| Създател≠ | Wasserman, S. & Faust, K. | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. |
| Тип≠ | Structural analysis of directed graphs | Graph partitioning / modularity optimization |
| Основополагащ източник≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 | Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ |
| Други названия | directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning |
| Свързани≠ | 5 | 6 |
| Резюме≠ | Directed Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades. | 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. |
| ScholarGateНабор от данни ↗ |
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