Salīdzināt metodes

	Laika modulu analīze	Svērtā modulitātes analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2010	2004
Autors	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P.	Newman, M. E. J.
Tips	Community detection (temporal extension of modularity optimization)	Community structure optimization on weighted graphs
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.	Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131.
Citi nosaukumi	dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis	weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity
Saistītās	5	5
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.	Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully.