Σύγκριση μεθόδων

	Ανάλυση Συνεκτικότητας	Ανάλυση Διάχυσης Δικτύου
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2004	1927 (epidemic roots); network formalization 1990s–2000s
Δημιουργός	Newman, M. E. J. & Girvan, M.	Kermack, W. O. & McKendrick, A. G.
Τύπος	Community detection / graph partitioning	Simulation / analytical model
Θεμελιώδης πηγή	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.	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.
Εναλλακτικές ονομασίες	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity	diffusion on networks, information diffusion, contagion spreading model, network propagation model
Συναφείς	5	5
Σύνοψη	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.	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.