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| 시간적 사회 연결망 분석× | 시간적 커뮤니티 탐지× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2000s–2010s | 2010 |
| 창시자≠ | Moody, J.; Holme, P.; Saramäki, J. | Mucha, P. J. et al. |
| 유형≠ | Longitudinal network analysis | Network clustering algorithm |
| 원전≠ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. 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 | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| 관련≠ | 4 | 6 |
| 요약≠ | Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time. | 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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