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| 다층 시계열 네트워크 분석× | 시간적 커뮤니티 탐지× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2012–2014 | 2010 |
| 창시자≠ | Kivela, M. et al.; Holme, P. & Saramaki, J. | Mucha, P. J. et al. |
| 유형≠ | Network analysis framework | Network clustering algorithm |
| 원전≠ | Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ | 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 ↗ |
| 별칭 | MTNA, temporal multilayer network analysis, time-varying multilayer network analysis, dynamic multilayer network analysis | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| 관련≠ | 4 | 6 |
| 요약≠ | Multilayer temporal network analysis studies relational systems in which nodes interact through multiple distinct types of ties that all evolve over time. By modeling each relationship type as a separate layer and tracking how those layers change across time snapshots, the method reveals how cross-layer dynamics and temporal patterns jointly shape information flow, influence spread, and community structure. | Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution. |
| ScholarGate데이터셋 ↗ |
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