Сравнение методов

	Анализ ориентированных графов знаний	Анализ направленных социальных сетей
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2000s–2010s	1994
Автор метода	Hogan, A. et al. (formalized); roots in Berners-Lee, T. et al. (Semantic Web)	Wasserman, S. & Faust, K.
Тип	Graph-based knowledge representation and inference	Structural analysis of directed graphs
Основополагающий источник	Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G. D., Gutierrez, C., ... & Polleres, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), 1–37.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Другие названия	directed KG analysis, knowledge graph mining, directed semantic graph analysis, KG reasoning	directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis
Связанные	6	5
Сводка	Directed Knowledge Graph Analysis represents factual knowledge as a directed labeled multigraph of entities (nodes) and typed relations (directed edges), enabling structured reasoning, inference, and discovery over large heterogeneous datasets. The direction of edges encodes asymmetric relationships such as 'authored-by', 'causes', or 'is-a', making the graph semantically richer than undirected alternatives.	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.