Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ugunduzi wa Jumuiya× | Uchambuzi wa Mitandao Mingi× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Mitandao | Uchanganuzi wa Mitandao |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2002–2019 (algorithm family) | 2013–2014 (formal mathematical framework) |
| Mwanzilishi≠ | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) | Kivelä et al. (2014); De Domenico et al. (2013) |
| Aina≠ | Graph-partitioning / clustering algorithm family | Graph-theoretic network model |
| Chanzo asilia≠ | 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 ↗ | Kivelä, M. et al. (2014). Multilayer Networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ |
| Majina mbadala | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) | multiplex network analysis, multiplex networks, Çok Katmanlı Ağ Analizi (Multiplex Networks) |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | 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? | Multilayer network analysis is a graph-theoretic framework, formalised by Kivelä et al. (2014) and De Domenico et al. (2013), that represents the same set of nodes simultaneously across multiple relationship layers. Where a single-layer network collapses all relationships into one graph, the multilayer model preserves the distinct relational context of each layer — social platform, biological interaction type, or infrastructure tier — while also modelling how layers couple with each other through interlayer edges. |
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