Machine learningNetwork science
Network Diffusion Analysis
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.
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Sources
- 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. DOI: 10.1098/rspa.1927.0118 ↗
- Watts, D. J. & Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393, 440–442. DOI: 10.1038/30918 ↗
Related methods
Referenced by
Bayesian Network Diffusion AnalysisBayesian Social Network AnalysisCloseness CentralityDirected Network Diffusion AnalysisDynamic Exponential Random Graph ModelKnowledge Graph AnalysisModularity AnalysisMultilayer Network Diffusion AnalysisMultiplex Network AnalysisTemporal Network Diffusion AnalysisTemporal PageRankTemporal Social Network AnalysisWeighted Network Diffusion AnalysisWeighted Temporal Network Analysis