#Applications

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

1 post tagged with applications. ← All posts

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Pratik Dhanave · ·7 min read

Applications and When to Use GNNs

Graph neural networks are genuinely transformative for the right problems and needless complexity for the wrong ones. This closing post surveys where GNNs deliver real value — drug discovery, recommendation, fraud detection, knowledge graphs — and, just as importantly, gives the honest decision framework for when a GNN is worth its cost versus when a simpler approach wins. It turns the series into a judgment you can apply.

GNNs are transformative for the right problems (drug discovery, recommendation, fraud rings, knowledge graphs) and needless complexity for the wrong ones. The honest decision framework: use a GNN when structure carries signal and it beats a structure-ignorant baseline — and reach for something simpler when it doesn't.

#Graph Neural Networks#Applications#Engineering Judgment#Machine Learning#Deep 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.