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| Analiza društvenih mreža× | Centralnost svojstvenog vektora× | |
|---|---|---|
| Područje | Analiza mreža | Analiza mreža |
| Obitelj | Machine learning | Machine learning |
| Godina nastanka≠ | 1934 (sociometry); 1994 (modern formalization) | 1972 |
| Tvorac≠ | Moreno, J.L.; formalized by Wasserman & Faust | Bonacich, P. |
| Vrsta≠ | Structural/relational analysis framework | Centrality measure |
| Temeljni izvor≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Drugi nazivi | SNA, network analysis, sociometric analysis, relational analysis | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Srodne≠ | 5 | 6 |
| Sažetak≠ | 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. | 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. |
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