Statistical Analysis of Network Data: Structures and Models

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Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Explores causal inference through learning graph structures for causal reasoning from observational data.

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.

Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Explores graph statistics, random graph generation, network analysis, centrality measures, and clustering coefficients.

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

Explores statistical analysis of network data, covering noisy sampled networks, likelihood estimation, multilayer networks, and directed networks.