So sánh phương pháp

	Phân tích khuếch tán mạng theo thời gian	Phát hiện cộng đồng theo thời gian
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	2012	2010
Người khởi xướng	Holme, P. & Saramäki, J.	Mucha, P. J. et al.
Loại	Network analysis framework	Network clustering algorithm
Công trình gốc	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.
Tên gọi khác	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
Liên quan	5	6
Tóm tắt	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.