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| Mô hình đồ thị ngẫu nhiên mũ có trọng số× | Độ trung tâm bậc có trọng số× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2012 | 2004 |
| Người khởi xướng≠ | Krivitsky, P. N. | Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A. |
| Loại≠ | Statistical network model | Centrality measure for weighted networks |
| Công trình gốc≠ | Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128. DOI ↗ | Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ |
| Tên gọi khác | W-ERGM, valued ERGM, weighted p-star model, valued exponential random graph model | node strength, strength centrality, weighted node degree, WDC |
| Liên quan≠ | 4 | 6 |
| Tóm tắt≠ | The Weighted Exponential Random Graph Model (W-ERGM) extends the classic binary ERGM framework to networks whose edges carry quantitative values — such as frequency of contact, trade volume, or collaboration intensity. It models the entire valued-edge network as a probability distribution defined over all possible weighted graphs, enabling researchers to test whether structural patterns such as reciprocity, transitivity, or degree distribution arise beyond what chance alone would produce. | Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score. |
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