مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| آشکارسازی جامعه چندلایهای× | آشکارسازی جامعه× | |
|---|---|---|
| حوزه | تحلیل شبکه | تحلیل شبکه |
| خانواده≠ | Machine learning | Process / pipeline |
| سال پیدایش≠ | 2010–2014 | 2002–2019 (algorithm family) |
| پدیدآور≠ | Mucha, P. J. et al.; Kivela, M. et al. | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) |
| نوع≠ | Community detection algorithm for multilayer networks | Graph-partitioning / clustering algorithm family |
| منبع بنیادین≠ | Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. 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 ↗ |
| نامهای دیگر≠ | multilayer clustering, multiplex community detection, cross-layer community detection, MCD | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| مرتبط | 5 | 5 |
| خلاصه≠ | Multilayer community detection identifies groups of nodes that are densely connected across multiple types of relationships simultaneously. By coupling layers of a network — such as friendship, advice, and collaboration ties — it finds communities that are coherent not just within one relation type but across all of them, revealing structure that single-layer analysis would miss. | 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? |
| ScholarGateمجموعهداده ↗ |
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