Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Centraliteti i Ndërmjetësisë Kohore× | Analiza e Difuzionit në Rrjetet Temporale× | |
|---|---|---|
| Fusha | Analiza e rrjeteve | Analiza e rrjeteve |
| Familja | Machine learning | Machine learning |
| Viti i origjinës | 2012 | 2012 |
| Krijuesi≠ | Kim, H. & Anderson, R.; Holme, P. & Saramäki, J. | Holme, P. & Saramäki, J. |
| Lloji≠ | Centrality measure for temporal networks | Network analysis framework |
| Burimi themelues≠ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Emërtime të tjera | TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness | TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks |
| Të lidhura≠ | 6 | 5 |
| Përmbledhja≠ | Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot. | Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss. |
| ScholarGateSeti i të dhënave ↗ |
|
|