เปรียบเทียบวิธี

	PageRank แบบถ่วงน้ำหนัก	Weighted Eigenvector Centrality
สาขาวิชา	การวิเคราะห์เครือข่าย	การวิเคราะห์เครือข่าย
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2004	1987 (binary); 2010 (weighted generalization)
ผู้ริเริ่ม	Xing, W. & Ghorbani, A.	Bonacich, P. (binary); Opsahl, T. et al. (weighted extension)
ประเภท	Centrality measure / ranking algorithm	Spectral centrality measure
แหล่งต้นตำรับ	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.	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.
ชื่อเรียกอื่น	WPR, weighted page rank, edge-weighted PageRank, strength-based PageRank	WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige
ที่เกี่ยวข้อง	6	6
สรุป	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.	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.