方法对比

	Temporal Two-Mode Network Analysis	时间网络分析
领域	网络分析	网络分析
方法族	Machine learning	Process / pipeline
起源年份	1990s–2010s	2012
提出者	Borgatti, S. P. & Everett, M. G. (two-mode foundations); extended to temporal setting by multiple authors	Holme & Saramäki (2012) — seminal framework
类型	Network analysis technique	Dynamic graph analysis
开创性文献	Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
别名	temporal bipartite network analysis, dynamic two-mode network analysis, time-varying bipartite network analysis, longitudinal affiliation network analysis	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
相关	5	3
摘要	Temporal two-mode network analysis tracks relationships between two distinct classes of nodes — such as authors and publications, or actors and events — across multiple time points. By combining bipartite structure with longitudinal observation, it reveals how affiliation patterns, collaborations, and community memberships form, evolve, and dissolve over time.	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.