قارن الطرق

	تحليل الشبكات ثنائية الوضع الموزونة	تحليل النمطية
المجال	تحليل الشبكات	تحليل الشبكات
العائلة	Machine learning	Machine learning
سنة النشأة	1997 (two-mode); weighted extensions 2000s	2004
صاحب الطريقة	Borgatti, S. P. & Everett, M. G.	Newman, M. E. J. & Girvan, M.
النوع	Network structural analysis	Community detection / graph partitioning
المصدر التأسيسي	Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
الأسماء البديلة	weighted bipartite network analysis, valued two-mode network analysis, weighted affiliation network analysis, W2MNA	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
ذات صلة	6	5
الملخص	Weighted two-mode network analysis examines bipartite graphs in which two distinct node sets — such as actors and events, authors and papers, or species and habitats — are connected by edges carrying numerical weights that capture the strength, frequency, or intensity of each affiliation. Incorporating weights provides substantially richer structural insights than unweighted bipartite analysis.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.