Bandingkan metode

	Analisis Jaringan Sosial Temporal	Deteksi Komunitas Temporal
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2000s–2010s	2010
Pencetus	Moody, J.; Holme, P.; Saramäki, J.	Mucha, P. J. et al.
Tipe	Longitudinal network analysis	Network clustering algorithm
Sumber perintis	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	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.
Alias	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Terkait	4	6
Ringkasan	Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time.	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.