Bandingkan kaedah

	Analisis Penyebaran Rangkaian Berbobot	Weighted PageRank
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2004	2004
Pengasas	Barrat, A.; Newman, M. E. J.	Xing, W. & Ghorbani, A.
Jenis	Network diffusion model	Centrality measure / ranking algorithm
Sumber perintis	Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.	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.
Alias	WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion	WPR, weighted page rank, edge-weighted PageRank, strength-based PageRank
Berkaitan	6	6
Ringkasan	Weighted Network Diffusion Analysis models how information, influence, disease, or resources spread through a network whose edges carry quantitative strength values. By letting tie weights govern transition probabilities, the method produces more realistic spreading dynamics than binary-edge diffusion, revealing which high-traffic pathways dominate propagation in social, biological, and information networks.	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.