So sánh phương pháp

	Phân tích khuếch tán mạng theo thời gian	Độ trung tâm giữa hai điểm theo thời gian (Temporal Betweenness Centrality)
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	2012	2012
Người khởi xướng	Holme, P. & Saramäki, J.	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.
Loại	Network analysis framework	Centrality measure for temporal networks
Công trình gốc	Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Tên gọi khác	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness
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 Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot.