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Machine learningNetwork science

Dynamic Exponential Random Graph Model (Temporal ERGM)

ERGM tuli huuliza: kwa kuzingatia picha moja ya mtandao huu, ni nguvu gani za kimuundo (kurudiana, pembetatu, usambazaji wa digrii) zinaelezea topolojia yake vyema? ERGM hai huuliza swali lile lile katika vipindi vingi vya muda kwa wakati mmoja, ikiruhusu mtandao wa jana kuwa kigezo cha leo. Hii huwaruhusu watafiti kujaribu ikiwa uhusiano wa zamani unadumu, ikiwa motifs fulani za kimuundo huharakisha kuundwa kwa uhusiano, na ikiwa sifa za nodi husababisha kufunguka—yote ndani ya mfumo mmoja thabiti wa uwezekano.

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Vyanzo

  1. Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605. DOI: 10.1214/09-EJS548
  2. Krivitsky, P. N., & Handcock, M. S. (2014). A separable model for dynamic networks. Journal of the Royal Statistical Society: Series B, 76(1), 29–46. DOI: 10.1111/rssb.12014

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Dynamic Exponential Random Graph Model (Temporal ERGM). ScholarGate. https://scholargate.app/sw/network-analysis/dynamic-exponential-random-graph-model

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ScholarGateDynamic Exponential Random Graph Model (Dynamic Exponential Random Graph Model (Temporal ERGM)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/network-analysis/dynamic-exponential-random-graph-model · Seti ya data: https://doi.org/10.5281/zenodo.20539026