Sammenlign metoder

	Temporal Community Detection	Modularitetsanalyse
Fagområde	Netværksanalyse	Netværksanalyse
Familie	Machine learning	Machine learning
Oprindelsesår	2010	2004
Ophavsperson	Mucha, P. J. et al.	Newman, M. E. J. & Girvan, M.
Type	Network clustering algorithm	Community detection / graph partitioning
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.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Aliasser	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Relaterede	6	5
Resumé	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.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.