Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Modulāritātes analīze× | Sociālo tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2004 | 1934 (sociometry); 1994 (modern formalization) |
| Autors≠ | Newman, M. E. J. & Girvan, M. | Moreno, J.L.; formalized by Wasserman & Faust |
| Tips≠ | Community detection / graph partitioning | Structural/relational analysis framework |
| Pirmavots≠ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Citi nosaukumi | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity | SNA, network analysis, sociometric analysis, relational analysis |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | 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. |
| ScholarGateDatu kopa ↗ |
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