So sánh phương pháp

	Mô hình Khối Ngẫu nhiên Động	Phân tích tính mô-đun
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	2011	2004
Người khởi xướng	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.	Newman, M. E. J. & Girvan, M.
Loại	Generative probabilistic model	Community detection / graph partitioning
Công trình gốc	Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 82(2), 157–189.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Tên gọi khác	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Liên quan	5	5
Tóm tắt	The Dynamic Stochastic Block Model (DSBM) is a generative probabilistic framework that extends the static stochastic block model to networks observed across multiple time points. It jointly models community membership and community evolution, allowing researchers to detect and track latent groups and their structural changes over time in longitudinal network data.	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.