Salīdzināt metodes

	Svērtais eksponenciālais nejaušo grafu modelis (W-ERGM)	Svērtā modulitātes analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2012	2004
Autors	Krivitsky, P. N.	Newman, M. E. J.
Tips	Statistical network model	Community structure optimization on weighted graphs
Pirmavots	Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128.	Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131.
Citi nosaukumi	W-ERGM, valued ERGM, weighted p-star model, valued exponential random graph model	weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity
Saistītās	4	5
Kopsavilkums	The Weighted Exponential Random Graph Model (W-ERGM) extends the classic binary ERGM framework to networks whose edges carry quantitative values — such as frequency of contact, trade volume, or collaboration intensity. It models the entire valued-edge network as a probability distribution defined over all possible weighted graphs, enabling researchers to test whether structural patterns such as reciprocity, transitivity, or degree distribution arise beyond what chance alone would produce.	Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully.