เปรียบเทียบวิธี

	Dynamic Degree Centrality	การตรวจจับชุมชนแบบพลวัต
สาขาวิชา	การวิเคราะห์เครือข่าย	การวิเคราะห์เครือข่าย
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2012	2010 (key formalization); earlier work 2002–2009
ผู้ริเริ่ม	Holme, P. & Saramaki, J.; Kim, H. & Anderson, R.	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)
ประเภท	Centrality measure (temporal extension)	Graph clustering / community discovery
แหล่งต้นตำรับ	Holme, P. & Saramaki, 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.
ชื่อเรียกอื่น	time-varying degree centrality, temporal degree centrality, evolving degree centrality, DDC	DCD, temporal community detection, evolving community detection, dynamic graph clustering
ที่เกี่ยวข้อง	5	5
สรุป	Dynamic degree centrality extends the classical degree centrality measure to networks that change over time. Rather than counting a node's connections in a single static snapshot, it tracks how many contacts each node maintains across successive time windows or contact events, producing a time-resolved importance profile for every actor in the network.	Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.