เปรียบเทียบวิธี

	ค่ากลางองศาเชิงเวลา	Temporal Eigenvector Centrality
สาขาวิชา	การวิเคราะห์เครือข่าย	การวิเคราะห์เครือข่าย
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2011–2012	2011-2017
ผู้ริเริ่ม	Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.	Grindrod, P.; Higham, D. J.; Taylor, D. et al.
ประเภท	Centrality measure (temporal extension)	Centrality measure for temporal networks
แหล่งต้นตำรับ	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120.
ชื่อเรียกอื่น	time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC	dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality
ที่เกี่ยวข้อง	6	5
สรุป	Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window.	Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network.