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| 시간 고유벡터 중심성× | 시간적 사회 연결망 분석× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2011-2017 | 2000s–2010s |
| 창시자≠ | Grindrod, P.; Higham, D. J.; Taylor, D. et al. | Moody, J.; Holme, P.; Saramäki, J. |
| 유형≠ | Centrality measure for temporal networks | Longitudinal network analysis |
| 원전≠ | Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120. DOI ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| 별칭 | dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| 관련≠ | 5 | 4 |
| 요약≠ | Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network. | 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. |
| ScholarGate데이터셋 ↗ |
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