Linganisha mbinu

	Uchambuzi wa Usambaaji wa Mitandao ya Muda	Ugunduzi wa Jumuiya za Muda
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2012	2010
Mwanzilishi	Holme, P. & Saramäki, J.	Mucha, P. J. et al.
Aina	Network analysis framework	Network clustering algorithm
Chanzo asilia	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.
Majina mbadala	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Zinazohusiana	5	6
Muhtasari	Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss.	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.