Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse des réseaux sociaux× | Analyse de modularité× | |
|---|---|---|
| Domaine | Analyse de réseaux | Analyse de réseaux |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 1934 (sociometry); 1994 (modern formalization) | 2004 |
| Auteur d'origine≠ | Moreno, J.L.; formalized by Wasserman & Faust | Newman, M. E. J. & Girvan, M. |
| Type≠ | Structural/relational analysis framework | Community detection / graph partitioning |
| Source fondatrice≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| Alias | SNA, network analysis, sociometric analysis, relational analysis | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks. |
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