เปรียบเทียบวิธี

	การเป็นศูนย์กลางแบบใกล้ชิดพลวัต	การตรวจจับชุมชนเชิงเวลา
สาขาวิชา	การวิเคราะห์เครือข่าย	การวิเคราะห์เครือข่าย
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2010–2012	2010
ผู้ริเริ่ม	Tang, J. et al.; Holme, P. & Saramäki, J.	Mucha, P. J. et al.
ประเภท	Centrality measure for temporal networks	Network clustering algorithm
แหล่งต้นตำรับ	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.	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.
ชื่อเรียกอื่น	temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
ที่เกี่ยวข้อง	5	6
สรุป	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 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.