Advanced Architectures

Graph Neural Networks — Learning from Graph-Structured Data

GNNs generalize neural networks to irregular, non-Euclidean data structures by aggregating information from node neighborhoods. From molecules to social networks, GNNs learn powerful representations by passing messages along edges and updating node features iteratively.

Key point 1 — Message passing framework unifies GCN, GraphSAGE, and GAT architectures
Key point 2 — Over-smoothing limits practical depth to 2-3 layers in standard GNNs
Key point 3 — GNNs excel at molecular property prediction, link prediction, and social analysis

"In graphs, structure is the signal — and GNNs learn to read it."

Graph Neural Networks — Deep Dive

GNNs learn on graph-structured data by aggregating information from node neighborhoods. They generalize neural networks to irregular, non-Euclidean data structures.

Graph Neural Networks — Deep Dive

Graph Neural Networks — Learning from Graph-Structured Data

Graph Neural Networks — Deep Dive

Graph Data

Message Passing Framework

Graph Convolutional Network (GCN)

GraphSAGE

Graph Attention Network (GAT)

Over-Smoothing

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