Сравнение методов

	Анализ направленной модулярности	Анализ направленных социальных сетей
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2008	1994
Автор метода	Leicht, E. A. & Newman, M. E. J.	Wasserman, S. & Faust, K.
Тип	Community detection / graph partitioning	Structural analysis of directed graphs
Основополагающий источник	Leicht, E. A., & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Другие названия	directed community detection via modularity, directed Q-modularity, digraph modularity optimization, Leicht-Newman modularity	directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis
Связанные	5	5
Сводка	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.	Directed Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades.