Сравнение методов

	Анализ взвешенных графов знаний	Взвешенная центральность по собственному вектору
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2010s–present	1987 (binary); 2010 (weighted generalization)
Автор метода	Hogan et al. and the broader knowledge graph community	Bonacich, P. (binary); Opsahl, T. et al. (weighted extension)
Тип	Network analysis variant	Spectral centrality measure
Основополагающий источник	Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1–37.	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.
Другие названия	WKGA, weighted KG analysis, confidence-weighted knowledge graph, weighted semantic network analysis	WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige
Связанные	6	6
Сводка	Weighted Knowledge Graph Analysis extends standard knowledge graph methods by assigning numerical weights — such as confidence scores, co-occurrence frequencies, or relation strengths — to edges between entities. These weights allow analysts to prioritise high-confidence triples, find the most influential paths, and compute weight-aware centrality and community structure in large structured knowledge bases.	Weighted eigenvector centrality extends the classic eigenvector centrality measure to graphs where edges carry numerical weights, scoring each node proportionally to the sum of its neighbors' scores multiplied by the connecting edge weights. Nodes score highly not just by having many connections but by being strongly linked to other influential nodes, making the measure sensitive to both tie strength and network position simultaneously.