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| Directed Community Detection× | 방향성 사회 연결망 분석× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2008 | 1994 |
| 창시자≠ | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. | Wasserman, S. & Faust, K. |
| 유형≠ | Graph partitioning / modularity optimization | Structural analysis of directed graphs |
| 원전≠ | 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 |
| 별칭 | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning | directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. | 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. |
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