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| 사회 연결망 분석× | 근접 중심성× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1934 (sociometry); 1994 (modern formalization) | 1950 (formalized 1979) |
| 창시자≠ | Moreno, J.L.; formalized by Wasserman & Faust | Bavelas, A.; formalized by Freeman, L. C. |
| 유형≠ | Structural/relational analysis framework | Node-level centrality index |
| 원전≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| 별칭 | SNA, network analysis, sociometric analysis, relational analysis | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| 관련≠ | 5 | 6 |
| 요약≠ | 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. | Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts. |
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