This topic presents the computational foundations of diffusion and contagion modeling within social networks, emphasizing the interplay between topology and transmission dynamics. Path-finding algorithms such as Breadth-First Search (BFS) and Dijkstra’s algorithm are introduced as the algorithmic backbone for tracing information flow and computing latency across unweighted and weighted networks. The role of topology in governing diffusion efficiency is examined, showing how small-world structures accelerate global reach while modular communities impose structural boundaries.

Epidemic models—specifically the SIR and SIS frameworks—are described as agent-based processes simulating the temporal progression of contagion, governed by parameters β (infection rate) and γ (recovery rate). The concept of the epidemic threshold (λc) is discussed as a critical value linking network degree distribution to global diffusion stability. Finally, simulation experiments are detailed for measuring propagation speed, validating stochastic models, and correlating diffusion outcomes with structural metrics such as degree, betweenness, and clustering. The topic concludes that diffusion and contagion are not random but structurally conditioned processes that translate topology into dynamic social behavior.

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