Compară metode

	Analiza difuziunii în rețea	Analiza modularității
Domeniu	Analiza rețelelor	Analiza rețelelor
Familie	Machine learning	Machine learning
Anul apariției	1927 (epidemic roots); network formalization 1990s–2000s	2004
Autorul original	Kermack, W. O. & McKendrick, A. G.	Newman, M. E. J. & Girvan, M.
Tip	Simulation / analytical model	Community detection / graph partitioning
Sursa seminală	Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Denumiri alternative	diffusion on networks, information diffusion, contagion spreading model, network propagation model	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Înrudite	5	5
Rezumat	Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally.	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.