방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 시간적 매개 중심성× | 시간적 차수 중심성× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2012 | 2011–2012 |
| 창시자≠ | Kim, H. & Anderson, R.; Holme, P. & Saramäki, J. | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. |
| 유형≠ | Centrality measure for temporal networks | Centrality measure (temporal extension) |
| 원전≠ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| 별칭 | TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC |
| 관련 | 6 | 6 |
| 요약≠ | Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot. | 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. |
| ScholarGate데이터셋 ↗ |
|
|