Handling Network Data

This lecture covers various aspects of handling network data, including topics such as applied data analysis, exercises on handling networks, types of graphs, weighted and bipartite graphs, projections of bipartite graphs, patient networks, and networks as graphs. It also delves into the representation of graphs on computers using adjacency matrices and edge lists, as well as properties of real-world networks like sparsity, degree distribution, triadic closure, community structure, and average shortest-path length. The lecture further explores centrality measures such as degree centrality, Katz centrality, betweenness centrality, and PageRank centrality, emphasizing their importance in analyzing network structures.