Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Vērsta divu režīmu tīkla analīze× | Vērstā sociālo tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1997 | 1994 |
| Autors≠ | Borgatti, S. P. & Everett, M. G. | Wasserman, S. & Faust, K. |
| Tips≠ | Structural network analysis | Structural analysis of directed graphs |
| Pirmavots≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications (Ch. 8). Cambridge University Press. ISBN: 978-0-521-38707-1 | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Citi nosaukumi | directed bipartite network analysis, asymmetric affiliation network analysis, directed actor-event network analysis, directed two-mode graph analysis | directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Directed two-mode network analysis studies bipartite graphs in which nodes belong to two distinct sets — such as actors and events, authors and papers, or firms and markets — and edges carry a direction, capturing asymmetric relationships like citation, referral, or endorsement. Combining the duality of two-mode structure with directed tie semantics reveals flow patterns and influence asymmetries that undirected or single-mode analyses would miss. | Directed Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades. |
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