#Node Classification

Articles about Node Classification — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with node classification. ← All posts

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Pratik Dhanave · ·6 min read

Graph Convolutional Networks — The First Great GNN

The graph convolutional network is the "linear regression" of graph deep learning: the simplest architecture worth knowing, the one that made GNNs practical, and the baseline every other method is compared against. It's message passing with the most natural possible aggregation — average your neighbors — and understanding exactly what it does, and where its simplicity bites, is the foundation for everything more sophisticated.

The GCN is the 'logistic regression' of graph deep learning — message passing with the simplest aggregation (degree-normalized averaging), the strong baseline every method is compared against. Why it shines at semi-supervised node classification (label propagation under homophily), and the limits that motivate GraphSAGE and GAT.

#Graph Neural Networks#GCN#Deep Learning#Node Classification#Machine Learning

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.