Lecture

Statistical Analysis of Network Data: Structures and Models

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Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

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Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Learning from the Interconnected World with Graphs

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Graph Neural Networks: Interconnected World

Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.

Graphical Models: Representing Probabilistic Distributions

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

Graph Algorithms: Modeling and Traversal

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Independence Polynomial of Dependency Graph

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Handling Network Data

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

Handling Networks: Graph Theory

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

Distances and Motif Counts

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

Graph Algorithms: Modeling and Representation

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

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Statistical Analysis of Network Data

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Graphs: Properties and Representations

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Statistical Analysis of Network Data: Noisy Sampled Networks

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