Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Kaalutud PageRank× | Vahel asuvus (Betweenness Centrality)× | |
|---|---|---|
| Valdkond | Võrgustikuanalüüs | Võrgustikuanalüüs |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2004 | 1977 |
| Looja≠ | Xing, W. & Ghorbani, A. | Freeman, L. C. |
| Tüüp≠ | Centrality measure / ranking algorithm | Centrality measure |
| Algallikas≠ | 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. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Rööpnimetused | WPR, weighted page rank, edge-weighted PageRank, strength-based PageRank | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Seotud | 6 | 6 |
| Kokkuvõte≠ | 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. | Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes. |
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