Advanced Topics
Graph Neural Networks — Learning from Connections and Relationships
Explore graph neural networks (GNNs) for learning on structured data with complex relationships. Perfect for social networks, molecular analysis, and recommendation systems.
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Message Passing — How nodes aggregate information from neighbors
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Graph Convolutional Networks — The foundational GNN architecture
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Graph Attention Networks — Learning which connections matter most
"Everything is connected, and those connections are where the real learning happens."
Graph Neural Networks — Complete Guide
GNNs learn from graph-structured data — social networks, molecules, knowledge graphs.
Why GNNs?
Graph Data Structure
Message Passing
Where:
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= node v's representation
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= neighbors of v
Message Passing Visualization
GCN (Graph Convolutional Network)
GCN Architecture
GNN Variants
GCN (Graph Convolutional Network):
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Spectral-based
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Averaging neighbor features
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Simple and effective
GraphSAGE:
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Sample neighbors (scalable)
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Multiple aggregation functions
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Inductive (works on new nodes)
GAT (Graph Attention Network):
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Attention over neighbors
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Learn which neighbors matter
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More expressive than GCN
GNN Variants Comparison
Key Takeaways
What to Learn Next
-> Neural Networks Fundamentals — Perceptrons to Deep Learning
Learn about neural networks fundamentals — perceptrons to deep learning.
-> Convolutional Neural Networks — Complete Guide for Vision
Learn about convolutional neural networks — complete guide for vision.
-> Transformers — Attention Is All You Need Complete Guide
Learn about transformers — attention is all you need complete guide.
-> Clustering — K-Means, DBSCAN, Hierarchical Complete Guide
Learn about clustering — k-means, dbscan, hierarchical complete guide.
-> Recommendation Systems — Collaborative and Content-Based Filtering
Learn about recommendation systems — collaborative and content-based filtering.
-> ML System Design — Architecture and Production Patterns
Learn about ml system design — architecture and production patterns.