Sammenlign metoder

	Temporal Modularitetsanalyse	Temporal Community Detection
Fagområde	Netværksanalyse	Netværksanalyse
Familie	Machine learning	Machine learning
Oprindelsesår	2010	2010
Ophavsperson	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P.	Mucha, P. J. et al.
Type	Community detection (temporal extension of modularity optimization)	Network clustering algorithm
Oprindelig kilde	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.
Aliasser	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
Relaterede	5	6
Resumé	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.