Vertaile menetelmiä

	Bayesiläinen yhteisöntunnistus	Monikerroksinen yhteisötunnistus
Tieteenala	Verkostoanalyysi	Verkostoanalyysi
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2001–2014	2010–2014
Kehittäjä	Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.	Mucha, P. J. et al.; Kivela, M. et al.
Tyyppi	Probabilistic generative model / inference	Community detection algorithm for multilayer networks
Alkuperäislähde	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.	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.
Rinnakkaisnimet	Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning	multilayer clustering, multiplex community detection, cross-layer community detection, MCD
Liittyvät	5	5
Tiivistelmä	Bayesian community detection infers latent group structure in networks by treating community membership as unobserved variables and using Bayesian inference — typically via Markov chain Monte Carlo or variational methods — to compute a posterior distribution over all plausible partitions. Unlike modularity optimisation, it selects the number of communities from data and provides principled uncertainty estimates for every node assignment.	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.