Depth Refinement and Normal Estimation

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

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Graph Algorithms II: Traversal and Paths

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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.

Learning from the Interconnected World with Graphs

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Covers the terminology related to the graph of a function and intervals.

Stochastic Blockmodel Estimation

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Explores graph theory in brain connectomics, MRI applications, network analysis relevance, and individual fingerprinting.

Graph Machine Learning

Delves into graph-enhanced machine learning, focusing on fraud detection, malware detection, and recommendation systems.

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Covers matrices, linear applications, vector spaces, and bijective functions.