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| Optimal Matching Analysis× | 사회 연결망 분석× | |
|---|---|---|
| 분야≠ | Sociology | 네트워크 분석 |
| 계열≠ | Process / pipeline | Machine learning |
| 기원 연도≠ | 1970 (algorithm); 1980s (sociology) | 1934 (sociometry); 1994 (modern formalization) |
| 창시자≠ | Needleman & Wunsch (algorithm); Andrew Abbott (sociological use) | Moreno, J.L.; formalized by Wasserman & Faust |
| 유형≠ | Edit-distance dissimilarity between categorical sequences | Structural/relational analysis framework |
| 원전≠ | Abbott, A., & Tsay, A. (2000). Sequence analysis and optimal matching methods in sociology: review and prospect. Sociological Methods & Research, 29(1), 3–33. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| 별칭 | optimal matching, OMA, edit-distance sequence comparison, Levenshtein sequence distance | SNA, network analysis, sociometric analysis, relational analysis |
| 관련 | 5 | 5 |
| 요약≠ | Optimal matching analysis measures how dissimilar two categorical sequences are by computing the minimum total cost of editing one sequence into the other through substitution and insertion/deletion operations. Borrowed from computer science and molecular biology and introduced to sociology by Andrew Abbott, it supplies the pairwise distances that underpin sequence analysis of careers, family histories, and other life-course trajectories. | 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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