Lecture
This lecture covers the handling of network data, including types of graphs, representing graphs on computers, properties of real-world networks, and measuring node importance. It discusses concepts such as node centrality, degree distribution, triadic closure, community structure, and navigability. The lecture also explores different centrality measures like degree centrality, closeness centrality, betweenness centrality, Katz centrality, and PageRank centrality, emphasizing the importance of linear algebra in understanding network structures.