Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ эго-сетей× | Предсказание связей× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1992 (Burt); foundational measurement formalised by Marsden 2002 | 2003 |
| Автор метода≠ | Ronald S. Burt (structural holes framework); Peter V. Marsden (egocentric measures) | — |
| Тип≠ | Descriptive / relational network analysis | Network inference task |
| Основополагающий источник≠ | Burt, R.S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press. ISBN: 9780674843714 | Liben-Nowell, D. & Kleinberg, J. (2007). The Link-Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, 58(7), 1019-1031. DOI ↗ |
| Другие названия≠ | personal network analysis, egocentric network analysis, Ego Ağı Analizi (Personal Network Analysis) | Bağlantı Tahmini (Link Prediction), missing link prediction, future link prediction, edge prediction |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Ego network analysis examines the personal network of a focal individual — the ego — by mapping their direct contacts (alters) and the ties those contacts share with one another. Formalised through Ronald Burt's structural holes framework (1992) and Marsden's egocentric measurement approach (2002), the method produces ego-level indicators such as network size, density, constraint, and brokerage role that reveal how each individual's social position shapes their access to information, resources, and influence. | Link prediction is a network-analysis task that estimates which edges are missing from an observed graph or which edges are likely to form in the future. Formalised by Liben-Nowell and Kleinberg (2003, 2007), it covers a spectrum of approaches — from simple structural similarity indices such as Common Neighbors, Jaccard coefficient, and Adamic-Adar, to matrix factorisation, and graph neural network (GNN) methods — and is evaluated with AUC and Average Precision to account for the heavily imbalanced ratio of real to non-existing edges. |
| ScholarGateНабор данных ↗ |
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