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Explores belief propagation on graphs, emphasizing normalization, recursive relations, and iterative computation of the partition function.
Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.
Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.
Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.
