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| 시간적 근접 중심성× | 시간적 차수 중심성× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2011 | 2011–2012 |
| 창시자≠ | Pan, R. K. & Saramaki, J. | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. |
| 유형≠ | Centrality measure (temporal) | Centrality measure (temporal extension) |
| 원전≠ | Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105. DOI ↗ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| 별칭 | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC |
| 관련 | 6 | 6 |
| 요약≠ | Temporal closeness centrality extends the classical closeness measure to time-varying networks by replacing static shortest paths with time-respecting (foremost) paths. It quantifies how quickly a node can reach all other nodes when interactions occur at specific moments in time, giving a more realistic picture of information flow, disease spread, and influence in dynamic systems. | Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window. |
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