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| Mô hình Đồ thị Ngẫu nhiên Lũy thừa Động× | Phân tích mạng thời gian× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ≠ | Machine learning | Process / pipeline |
| Năm ra đời≠ | 2010–2014 | 2012 |
| Người khởi xướng≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | Holme & Saramäki (2012) — seminal framework |
| Loại≠ | Probabilistic graphical model (temporal) | Dynamic graph analysis |
| Công trình gốc≠ | Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ |
| Tên gọi khác≠ | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Liên quan≠ | 4 | 3 |
| Tóm tắt≠ | The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about longitudinal network change. | Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system. |
| ScholarGateBộ dữ liệu ↗ |
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