Graph Statistics: Random Graphs, Graph Homomorphisms, and Network Analysis

This lecture by the instructor covers topics related to graph statistics, including random graph generation, graph homomorphisms, and network analysis. The lecture delves into the definition of functional t(F, G) for two graphs F and G, the metric for comparing networks, and practical approaches for comparing graphs. It also discusses network descriptors, fitting network models, and non-parametric summaries such as centrality measures like closeness centrality, harmonic centrality, and betweenness centrality. The lecture concludes with a discussion on clustering coefficients and parametric properties of graphs.