Machine learningNetwork science

Dinamička centralnost blizine

Dinamička centralnost blizine proširuje klasičnu centralnost blizine na vremenske mreže izračunavanjem najkraćih vremenski uvjetovanih putanja — putanja koje prelaze rubove kronološkim redoslijedom — i prosječno inverznih udaljenosti kroz sve vremenske prozore. Otkriva koje su čvorove najučinkovitije dosegnuti unutar mreže koja se razvija, prateći kako centralnost čvora raste i pada kako veze nestaju i pojavljuju se tijekom vremena.

Otvorite u MethodMindUskoroVideoUskoroDownload slides

Pročitajte cijelu metodu

Samo za članove

Prijavite se besplatnim računom kako biste pročitali ovaj odjeljak.

Prijavite se

Method map

The neighbourhood of related methods — select a node to explore.

Izvori

  1. Tang, J., Musolesi, M., Mascolo, C., Latora, V. & Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. Proceedings of the 3rd Workshop on Social Network Systems (SNS '10). ACM. DOI: 10.1145/1852658.1852661
  2. Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI: 10.1016/j.physrep.2012.03.001

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Dynamic Closeness Centrality in Temporal Networks. ScholarGate. https://scholargate.app/hr/network-analysis/dynamic-closeness-centrality

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

Compare side by side
ScholarGateDynamic Closeness Centrality (Dynamic Closeness Centrality in Temporal Networks). Preuzeto 2026-06-15 s https://scholargate.app/hr/network-analysis/dynamic-closeness-centrality · Skup podataka: https://doi.org/10.5281/zenodo.20539026