เปรียบเทียบวิธี

	Weighted Eigenvector Centrality	PageRank แบบถ่วงน้ำหนัก
สาขาวิชา	การวิเคราะห์เครือข่าย	การวิเคราะห์เครือข่าย
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	1987 (binary); 2010 (weighted generalization)	2004
ผู้ริเริ่ม	Bonacich, P. (binary); Opsahl, T. et al. (weighted extension)	Xing, W. & Ghorbani, A.
ประเภท	Spectral centrality measure	Centrality measure / ranking algorithm
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige	WPR, weighted page rank, edge-weighted PageRank, strength-based PageRank
ที่เกี่ยวข้อง	6	6
สรุป	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.