So sánh phương pháp

	Độ trung tâm gần động	Phân tích mạng xã hội theo thời gian
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	2010–2012	2000s–2010s
Người khởi xướng	Tang, J. et al.; Holme, P. & Saramäki, J.	Moody, J.; Holme, P.; Saramäki, J.
Loại	Centrality measure for temporal networks	Longitudinal network analysis
Công trình gốc	Tang, J., Musolesi, M., Mascolo, C., Latora, V. & Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. Proceedings of the 3rd Workshop on Social Network Systems (SNS '10). ACM.	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Tên gọi khác	temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA
Liên quan	5	4
Tóm tắt	Dynamic closeness centrality extends classic closeness centrality to temporal networks by computing shortest time-respecting paths — paths that traverse edges in chronological order — and averaging inverse distances across all time windows. It reveals which nodes are most efficiently reached within an evolving network, tracking how a node's centrality rises and falls as connections appear and disappear over time.	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.