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| 동적 양분 네트워크 분석× | 시간적 커뮤니티 탐지× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2000s–2012 | 2010 |
| 창시자≠ | Borgatti, S. P. & Halgin, D. S. (affiliation networks); Holme, P. & Saramäki, J. (temporal networks) | Mucha, P. J. et al. |
| 유형≠ | Longitudinal bipartite network analysis | Network clustering algorithm |
| 원전≠ | Borgatti, S. P., & Halgin, D. S. (2011). Analyzing affiliation networks. In J. Scott & P. J. Carrington (Eds.), The SAGE Handbook of Social Network Analysis (pp. 417–433). SAGE. link ↗ | 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 | Dynamic bipartite network analysis, Temporal two-mode network analysis, Longitudinal affiliation network analysis, Dynamic actor-event network analysis | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| 관련 | 6 | 6 |
| 요약≠ | Dynamic two-mode network analysis studies bipartite networks — structures with two distinct node types, such as actors and events or authors and papers — as they evolve over time. By tracking how memberships, affiliations, and co-participations change across temporal snapshots, it reveals the emergence, dissolution, and reorganization of collaborative or membership structures that static analysis would miss. | 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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