This topic explores the computational methods and theoretical underpinnings of analyzing evolving social networks through temporal data. Static graphs are reconceptualized as dynamic systems, where nodes and edges change over discrete or continuous time intervals. Time-stamped data are segmented into snapshots or link streams, allowing for the sequential application of traditional SNA metrics to study the emergence, persistence, and dissolution of ties. Edge turnover, persistence ratios, and rates of change in centrality are introduced as quantitative measures of structural stability and volatility.

Visualization techniques are emphasized, including consistent-layout temporal animations using Gephi and Python libraries to depict how networks “breathe” over time. Generative models—particularly the Barabási–Albert Preferential Attachment and Watts–Strogatz rewiring mechanisms—are presented as computational analogues of social evolution, illustrating how local probabilistic rules produce global order. These models are interpreted as representations of fundamental social processes such as popularity, innovation, and triadic closure. The topic concludes that dynamic network analysis bridges computation and sociology by treating time not as context but as structure—an integral dimension of how relationships form, decay, and reconfigure.

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