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Graph Neural Networks: Architecture, Mathematics, Formulation, and Applications by fardin.hossain.auritro is a document available to read on EtoBox.
What is Graph Neural Networks: Architecture, Mathematics, Formulation, and Applications about?
The document provides an overview of Graph Neural Networks (GNNs), detailing their architecture, mathematical formulation, and various applications across domains such as chemistry, recommendation systems, and citation networks. It discusses the importance of graphs in representing relational data and explains key concepts such as message passing, aggregation, and different GNN models like GCN and GAT. Additionally, the document includes practical examples, case studies, and training objectives relevant to
- Author
- fardin.hossain.auritro
- Language
- EN