Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika divu-modu tīklu analīze× | Modulāritātes analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1990s–2010s | 2004 |
| Autors≠ | Borgatti, S. P. & Everett, M. G. (two-mode foundations); extended to temporal setting by multiple authors | Newman, M. E. J. & Girvan, M. |
| Tips≠ | Network analysis technique | Community detection / graph partitioning |
| Pirmavots≠ | Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| Citi nosaukumi | temporal bipartite network analysis, dynamic two-mode network analysis, time-varying bipartite network analysis, longitudinal affiliation network analysis | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Temporal two-mode network analysis tracks relationships between two distinct classes of nodes — such as authors and publications, or actors and events — across multiple time points. By combining bipartite structure with longitudinal observation, it reveals how affiliation patterns, collaborations, and community memberships form, evolve, and dissolve over time. | 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. |
| ScholarGateDatu kopa ↗ |
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