방법 비교

	동적 모듈성 분석	커뮤니티 탐지
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Process / pipeline
기원 연도	2010	2002–2019 (algorithm family)
창시자	Mucha, P. J.; Porter, M. A.; and colleagues	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
유형	Community detection on temporal networks	Graph-partitioning / clustering algorithm family
원전	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.	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.
별칭	dynamic community structure analysis, temporal modularity optimization, evolving community detection, time-varying modularity	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
관련	5	5
요약	Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giving researchers a principled view of structural change in longitudinal network data.	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?