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Graph Neural Networks: Essentials and Use Cases
Hardcover
General ComputersGeneral MathematicsProbability & Statistics
ISBN13: 9783031885372
Publisher: Springer
Published: Jul 26 2025
Pages: 418
Weight: 1.74
Height: 1.00 Width: 6.14 Depth: 9.21
Language: English
- Graph Convolutional Networks (GCNs): These networks learn from a node's local neighborhood by aggregating information from adjacent nodes, updating the node's representation in the process. Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance. Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data.
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