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This lecture by the instructor covers the stochastic properties of network data, including network structures, models, and statistics. Topics include graph theory, network models like Erdős-Rényi and stochastic block models, network statistics such as subgraph counting and centrality measures, and network sampling methods. The lecture also delves into latent space models, network generative mechanisms, and additional topics like multilayer networks and link prediction.