Linganisha mbinu

	Uchambuzi wa Modularityi yenye Uzito	Uchanganuzi wa Modularity
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2004	2004
Mwanzilishi	Newman, M. E. J.	Newman, M. E. J. & Girvan, M.
Aina	Community structure optimization on weighted graphs	Community detection / graph partitioning
Chanzo asilia	Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Majina mbadala	weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Zinazohusiana	5	5
Muhtasari	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.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.