Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Exponential Random Graph Model (ERGM / p*)× | Link Prediction× | |
|---|---|---|
| Fagområde | Netværksanalyse | Netværksanalyse |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1986 (foundational); modern ERGM framework 1996–2007 | 2003 |
| Ophavsperson≠ | Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007) | — |
| Type≠ | Probabilistic generative network model | Network inference task |
| Oprindelig kilde≠ | Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191. DOI ↗ | 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 ↗ |
| Aliasser | ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*) | Bağlantı Tahmini (Link Prediction), missing link prediction, future link prediction, edge prediction |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | The Exponential Random Graph Model (ERGM), also known as the p* model, is a statistical framework for network analysis that models the probability of an observed network as a function of its local structural features — such as reciprocity, triangles, and degree distribution. Developed from the foundational work of Frank and Strauss (1986) and extended into the modern framework by Wasserman and Pattison (1996) and Robins et al. (2007), ERGM is the inferential standard for social network analysis, capable of testing whether observed network structures arise by chance or reflect genuine social processes. | 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. |
| ScholarGateDatasæt ↗ |
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