Automorphism Groups: Essential Chief Series

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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.

Graph Algorithms: Modeling and Traversal

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Graph Algorithms: Modeling and Representation

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

Groupes résolubles

Explores solvable groups, group actions, normalizers, and stabilizers in group theory.

Learning from the Interconnected World with Graphs

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Monster Group: Representation

Explores the Monster group, a sporadic simple group with a unique representation theory.

Belief Propagation

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Linear Algebra: Diagonalization

Explores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.

Graph Algorithms II: Traversal and Paths

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Covers self-similar groups and automorphisms of rooted trees, exploring residually finite groups and congruence subgroups.