Statistical Analysis of Network Data: Noisy Sampled Networks

This lecture delves into the statistical analysis of network data, exploring topics such as noisy sampled networks, likelihood estimation for directed networks, and multilayer networks. The instructor discusses the class of ERGMs, network sampling methods like relational and hyperedge sampling, and the Barabasi-Albert preferential attachment model. The lecture also covers multilayer networks, including the Axelrod model, multiplex networks, and edge-labeled multigraphs. Furthermore, the likelihood estimation for the B-model is explained, emphasizing the degree distribution and maximum likelihood estimates. Directed networks are examined, highlighting asymmetric relationships in social networks and the complexity of spreading rumors in directed networks.