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| Weighted PageRank× | Gradzentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2004 | 1978 |
| Urheber≠ | Xing, W. & Ghorbani, A. | Freeman, L. C. |
| Typ≠ | Centrality measure / ranking algorithm | Node-level centrality measure |
| Wegweisende Quelle≠ | 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. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Aliasnamen | WPR, weighted page rank, edge-weighted PageRank, strength-based PageRank | node degree, degree score, DC, connectivity centrality |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | 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. | Degree centrality is the simplest and most intuitive measure of a node's importance in a network, defined as the number of direct ties a node has to other nodes. Normalized by dividing by the maximum possible ties, it allows comparison across networks of different sizes and is the starting point of almost every network analysis. |
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