เปรียบเทียบวิธี

	การวิเคราะห์กราฟความรู้แบบมีทิศทาง	Eigenvector Centrality
สาขาวิชา	การวิเคราะห์เครือข่าย	การวิเคราะห์เครือข่าย
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2000s–2010s	1972
ผู้ริเริ่ม	Hogan, A. et al. (formalized); roots in Berners-Lee, T. et al. (Semantic Web)	Bonacich, P.
ประเภท	Graph-based knowledge representation and inference	Centrality measure
แหล่งต้นตำรับ	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.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
ชื่อเรียกอื่น	directed KG analysis, knowledge graph mining, directed semantic graph analysis, KG reasoning	eigenvector centrality, EC, Bonacich centrality, power centrality
ที่เกี่ยวข้อง	6	6
สรุป	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.	Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network.