Bandingkan metode

	Deteksi Komunitas Temporal	Deteksi Komunitas Tertimbang
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2010	2004–2008
Pencetus	Mucha, P. J. et al.	Newman, M. E. J.; Blondel et al.
Tipe	Network clustering algorithm	Graph clustering / community detection
Sumber perintis	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: Theory and Experiment, 2008(10), P10008.
Alias	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
Terkait	6	6
Ringkasan	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.	Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation.