Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika tīklu analīze× | Sociālo tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime≠ | Process / pipeline | Machine learning |
| Izcelsmes gads≠ | 2012 | 1934 (sociometry); 1994 (modern formalization) |
| Autors≠ | Holme & Saramäki (2012) — seminal framework | Moreno, J.L.; formalized by Wasserman & Faust |
| Tips≠ | Dynamic graph analysis | Structural/relational analysis framework |
| Pirmavots≠ | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Citi nosaukumi≠ | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) | SNA, network analysis, sociometric analysis, relational analysis |
| Saistītās≠ | 3 | 5 |
| Kopsavilkums≠ | Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system. | 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. |
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