手法を比較

	重み付き固有ベクトル中心性	重み付きPageRank
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Machine learning
提唱年	1987 (binary); 2010 (weighted generalization)	2004
提唱者	Bonacich, P. (binary); Opsahl, T. et al. (weighted extension)	Xing, W. & Ghorbani, A.
種類	Spectral centrality measure	Centrality measure / ranking algorithm
原典	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.	Xing, W., & Ghorbani, A. (2004). Weighted PageRank algorithm. Proceedings of the Second Annual Conference on Communication Networks and Services Research (CNSR '04), pp. 305–314. IEEE.
別名	WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige	WPR, weighted page rank, edge-weighted PageRank, strength-based PageRank
関連	6	6
概要	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.	Weighted PageRank extends the classic PageRank algorithm to networks where edges carry different strengths or frequencies, distributing importance proportionally to both incoming and outgoing edge weights rather than treating all links equally. This makes it substantially more informative than binary PageRank in any network where connection strength matters.