Salīdzināt metodes

	Laika modulu analīze	Temporālā kopienu noteikšana
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2010	2010
Autors	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P.	Mucha, P. J. et al.
Tips	Community detection (temporal extension of modularity optimization)	Network clustering algorithm
Pirmavots	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876-878.	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.
Citi nosaukumi	dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Saistītās	5	6
Kopsavilkums	Temporal modularity analysis extends standard modularity-based community detection to time-varying networks by treating each time slice as a network layer and coupling adjacent layers with inter-temporal links. This allows researchers to identify how communities form, persist, merge, split, and dissolve over time in dynamic relational data.	Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.