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| 동적 양분 네트워크 분석× | 동적 커뮤니티 탐지× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2000s–2012 | 2010 (key formalization); earlier work 2002–2009 |
| 창시자≠ | Borgatti, S. P. & Halgin, D. S. (affiliation networks); Holme, P. & Saramäki, J. (temporal networks) | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) |
| 유형≠ | Longitudinal bipartite network analysis | Graph clustering / community discovery |
| 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 | DCD, temporal community detection, evolving community detection, dynamic graph clustering |
| 관련≠ | 6 | 5 |
| 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. | Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research. |
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