قارن الطرق

	تحليل الشبكات الاجتماعية الزمنية	اكتشاف المجتمعات الزمنية
المجال	تحليل الشبكات	تحليل الشبكات
العائلة	Machine learning	Machine learning
سنة النشأة	2000s–2010s	2010
صاحب الطريقة	Moody, J.; Holme, P.; Saramäki, J.	Mucha, P. J. et al.
النوع	Longitudinal network analysis	Network clustering algorithm
المصدر التأسيسي	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.
الأسماء البديلة	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
ذات صلة	4	6
الملخص	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.