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| 방향성 사회 연결망 분석× | 사회 연결망 분석× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1994 | 1934 (sociometry); 1994 (modern formalization) |
| 창시자≠ | Wasserman, S. & Faust, K. | Moreno, J.L.; formalized by Wasserman & Faust |
| 유형≠ | Structural analysis of directed graphs | Structural/relational analysis framework |
| 원전 | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| 별칭 | directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis | SNA, network analysis, sociometric analysis, relational analysis |
| 관련 | 5 | 5 |
| 요약≠ | 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. | 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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