Salīdzināt metodes

	Vērstā modularitātes analīze	Starppriekšrocība (Betweenness Centrality)
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2008	1977
Autors	Leicht, E. A. & Newman, M. E. J.	Freeman, L. C.
Tips	Community detection / graph partitioning	Centrality measure
Pirmavots	Leicht, E. A., & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.	Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.
Citi nosaukumi	directed community detection via modularity, directed Q-modularity, digraph modularity optimization, Leicht-Newman modularity	Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness
Saistītās	5	6
Kopsavilkums	Directed modularity analysis extends the classic Newman-Girvan modularity framework to directed graphs, where edges carry a source and a destination. Formalized by Leicht and Newman in 2008, it partitions nodes into communities by maximizing a modularity score that accounts for each node's separate in-degree and out-degree in the null model, making it the standard approach for community detection in citation networks, information flows, and other asymmetric relational data.	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.