Bandingkan kaedah

	PageRank Berbilang Lapisan	Pusat Teras Eigenvector
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2015	1972
Pengasas	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.	Bonacich, P.
Jenis	Centrality measure (random-walk-based)	Centrality measure
Sumber perintis	De Domenico, M., Sole-Ribalta, A., Omodei, E., Gomez, S., & Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature Communications, 6, 6868.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
Alias	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank	eigenvector centrality, EC, Bonacich centrality, power centrality
Berkaitan	5	6
Ringkasan	Multilayer PageRank extends the classic PageRank random-walk centrality to networks that contain multiple interconnected layers — such as a social network where people are connected simultaneously via friendship, professional ties, and online platforms. By allowing a virtual walker to jump both within and across layers, the algorithm identifies nodes that are influential across the entire multilayer structure, not just within any single layer.	Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network.