Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Netwerkdiffusiemodellen× | Community Detection× | |
|---|---|---|
| Vakgebied | Netwerkanalyse | Netwerkanalyse |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1927 (epidemiological compartmental); 2003 (social influence cascade) | 2002–2019 (algorithm family) |
| Grondlegger≠ | Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003) | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) |
| Type≠ | Stochastic / deterministic simulation on graphs | Graph-partitioning / clustering algorithm family |
| Oorspronkelijke bron≠ | Kermack, W.O. & McKendrick, A.G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, 115(772), 700-721. DOI ↗ | 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 ↗ |
| Aliassen≠ | epidemic spreading models, compartmental models, influence propagation models, Ağ Yayılım Modelleri (SIR, SIS, Independent Cascade) | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| Verwant | 5 | 5 |
| Samenvatting≠ | Network diffusion models are a family of compartmental and probabilistic frameworks that simulate how information, disease, or innovation spreads across a connected system. Rooted in the mathematical epidemiology of Kermack and McKendrick (1927), the SIR and SIS models partition nodes into states and track transitions driven by contact rates and recovery probabilities. The Independent Cascade and Linear Threshold models, formalised by Kempe, Kleinberg, and Tardos (2003), extend this logic to social influence, modelling how activation propagates through a network one neighbour at a time. | 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? |
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