Linganisha mbinu

	Mfumo wa Uundaji wa Mfumo wa Mfumo wa Mtandao wa Kielektroniki Wenye Uzito	Uchambuzi wa Modularityi yenye Uzito
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2012	2004
Mwanzilishi	Krivitsky, P. N.	Newman, M. E. J.
Aina	Statistical network model	Community structure optimization on weighted graphs
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	4	5
Muhtasari	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.