Salīdzināt metodes

	Svērtā modulitātes analīze	Starppriekšrocība (Betweenness Centrality)
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2004	1977
Autors	Newman, M. E. J.	Freeman, L. C.
Tips	Community structure optimization on weighted graphs	Centrality measure
Pirmavots	Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131.	Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.
Citi nosaukumi	weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity	Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness
Saistītās	5	6
Kopsavilkums	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.	Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes.