Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Fællesskabsdetektion× | Link Prediction× | |
|---|---|---|
| Fagområde | Netværksanalyse | Netværksanalyse |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2002–2019 (algorithm family) | 2003 |
| Ophavsperson≠ | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) | — |
| Type≠ | Graph-partitioning / clustering algorithm family | Network inference task |
| Oprindelig kilde≠ | Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008. 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≠ | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) | Bağlantı Tahmini (Link Prediction), missing link prediction, future link prediction, edge prediction |
| Relaterede | 5 | 5 |
| Resumé≠ | Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network? | 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 ↗ |
|
|