Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Analiza modularității temporale× | Analiza modularității× | |
|---|---|---|
| Domeniu | Analiza rețelelor | Analiza rețelelor |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2010 | 2004 |
| Autorul original≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. | Newman, M. E. J. & Girvan, M. |
| Tip≠ | Community detection (temporal extension of modularity optimization) | Community detection / graph partitioning |
| Sursa seminală≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876-878. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| Denumiri alternative | dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| Înrudite | 5 | 5 |
| Rezumat≠ | Temporal modularity analysis extends standard modularity-based community detection to time-varying networks by treating each time slice as a network layer and coupling adjacent layers with inter-temporal links. This allows researchers to identify how communities form, persist, merge, split, and dissolve over time in dynamic relational data. | Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks. |
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