Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Svērta divu veidu tīklu analīze× | Svērtā modulitātes analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1997 (two-mode); weighted extensions 2000s | 2004 |
| Autors≠ | Borgatti, S. P. & Everett, M. G. | Newman, M. E. J. |
| Tips≠ | Network structural analysis | Community structure optimization on weighted graphs |
| 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. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI ↗ |
| Citi nosaukumi | weighted bipartite network analysis, valued two-mode network analysis, weighted affiliation network analysis, W2MNA | weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Weighted two-mode network analysis examines bipartite graphs in which two distinct node sets — such as actors and events, authors and papers, or species and habitats — are connected by edges carrying numerical weights that capture the strength, frequency, or intensity of each affiliation. Incorporating weights provides substantially richer structural insights than unweighted bipartite analysis. | Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully. |
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