concept

Graph Neural Networks

Graph Neural Networks (GNNs) are a class of deep learning models designed to operate directly on graph-structured data, capturing dependencies and relationships between nodes through message passing and aggregation mechanisms. They enable tasks like node classification, link prediction, and graph classification by learning representations that incorporate both node features and topological information.

Also known as: GNN, Graph Neural Network, Graph Convolutional Networks, GCN, Graph Networks
🧊Why learn Graph Neural Networks?

Developers should learn GNNs when working with non-Euclidean data such as social networks, molecular structures, recommendation systems, or knowledge graphs, where traditional neural networks like CNNs or RNNs are insufficient. They are essential for applications requiring relational reasoning, such as fraud detection in transaction networks, drug discovery with molecular graphs, or content recommendation based on user-item interactions.

Compare Graph Neural Networks

Graph Neural Networks vs Graph Kernels→Graph Neural Networks vs Relational Databases→Graph Neural Networks vs Traditional Neural Networks→

Learning Resources

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A Comprehensive Survey on Graph Neural Networks
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Graph Neural Networks - Stanford CS224W
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PyTorch Geometric Tutorial
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Graph Neural Networks Explained
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Related Tools

Deep LearningMachine LearningGraph TheoryPytorch GeometricTensorflow Gnn

Alternatives to Graph Neural Networks

Graph KernelsRelational DatabasesTraditional Neural Networks