Introduction to Graph Neural Networks and Their Applications

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This presentation provides a comprehensive overview of Graph Neural Networks (GNNs), starting with their definition, importance, and comparison with traditional models. It covers essential background concepts, including graph theory fundamentals and data representation. Core GNN mechanisms such as graph convolution, message passing, and node embedding are explored. Real-world applications, including recommendation systems, fraud detection, and biological analysis, are highlighted. Finally,...