Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Svaru kopienu noteikšana× | Sociālo tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2004–2008 | 1934 (sociometry); 1994 (modern formalization) |
| Autors≠ | Newman, M. E. J.; Blondel et al. | Moreno, J.L.; formalized by Wasserman & Faust |
| Tips≠ | Graph clustering / community detection | Structural/relational analysis framework |
| Pirmavots≠ | Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Citi nosaukumi | weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD | SNA, network analysis, sociometric analysis, relational analysis |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation. | 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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