Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ukaribu Uzito× | Uchanganuzi wa Mitandao ya Kijamii yenye Uzito× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Mitandao | Uchanganuzi wa Mitandao |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2010 | 2004–2010 |
| Mwanzilishi≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Barrat, A.; Opsahl, T. et al. |
| Aina≠ | Centrality measure (network analysis) | Network analysis framework |
| Chanzo asilia≠ | Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ | Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ |
| Majina mbadala | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart. | Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships. |
| ScholarGateSeti ya data ↗ |
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