Statistical Inference for Networks - Professor Gesine Reinert, University of Oxford

Explore statistical inference for networks in this comprehensive lecture by Professor Gesine Reinert from the University of Oxford. Delve into the fascinating world of network analysis, covering topics such as the six degrees of separation hypothesis, network statistics, and various mathematical models. Learn about key concepts including degree distribution, clustering coefficients, shortest distances, and motifs. Examine different network types, from marriage networks to the London network, and understand research questions surrounding network analysis. Discover mathematical models like power law, preferential attachment, and stochastic block models. Gain insights into estimation techniques such as maximum likelihood and method of moments. Investigate model testing, complications in network analysis, and explore additional topics like sampling. This lecture provides a thorough introduction to the field of network statistics and its applications in various contexts, including computational biology and social interactions.