手法を比較

	重み付き固有ベクトル中心性	次数中心性
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)	1978
提唱者	Bonacich, P. (binary); Opsahl, T. et al. (weighted extension)	Freeman, L. C.
種類	Spectral centrality measure	Node-level centrality measure
原典	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.	Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
別名	WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige	node degree, degree score, DC, connectivity centrality
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.	Degree centrality is the simplest and most intuitive measure of a node's importance in a network, defined as the number of direct ties a node has to other nodes. Normalized by dividing by the maximum possible ties, it allows comparison across networks of different sizes and is the starting point of almost every network analysis.