Statistical Analysis of Network Data: Noisy Sampled Networks

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Parameter Estimation

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.

Social and Information Networks 2: Evolution

Delves into social and information networks dynamics, including herding behavior, information cascades, preferential attachment, and the Friendship Paradox.

Distances and Motif Counts

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

Epidemic Spreading Models

Covers classical models of epidemic spreading and dynamics on networks with examples.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Statistics for Data Science: Introduction to Statistical Methods

Covers the fundamental concepts of statistics and their application in data science.

Introduction to Networks and Brain Networks

Introduces the fundamentals of network science, focusing on brain networks and their historical breakthroughs.

Inference for Stochastic Processes: Large Networks Analysis

Explores inference for stochastic processes, emphasizing large networks analysis and the need for new theories and methods.

Probability & Stochastic Processes

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Graph Coloring: Theory and Applications

Explores graph coloring theory, spectral clustering, community detection, and network structures.