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| 고유벡터 중심성× | 사회 연결망 분석× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1972 | 1934 (sociometry); 1994 (modern formalization) |
| 창시자≠ | Bonacich, P. | Moreno, J.L.; formalized by Wasserman & Faust |
| 유형≠ | Centrality measure | Structural/relational analysis framework |
| 원전≠ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| 별칭 | eigenvector centrality, EC, Bonacich centrality, power centrality | SNA, network analysis, sociometric analysis, relational analysis |
| 관련≠ | 6 | 5 |
| 요약≠ | Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network. | 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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