Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Многослойный PageRank× | Собственная центральность× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2015 | 1972 |
| Автор метода≠ | De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al. | Bonacich, P. |
| Тип≠ | Centrality measure (random-walk-based) | Centrality measure |
| Основополагающий источник≠ | 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. DOI ↗ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Другие названия | multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Связанные≠ | 5 | 6 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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