#Graph Neural Networks

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

8 posts tagged with graph neural networks. ← 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
Pratik Dhanave · ·6 min read

Training and Scaling GNNs — The Practical Realities

Graph neural networks look clean on paper and get messy in production, because training on graphs breaks assumptions that ordinary deep learning takes for granted. The data points aren't independent, the graph may not fit in memory, and the model fails in graph-specific ways. This post covers the practical realities of making GNNs actually work at scale — the problems you'll hit and the techniques that address them.

GNNs look clean on paper and get messy in production because the data points aren't independent. Why mini-batching and train/test splitting break on graphs (leakage through edges), how sampling scales GNNs to huge graphs, and the graph-specific failure modes (over-smoothing, over-squashing, homophily) that shape real systems.

#Graph Neural Networks#Training#Scalability#Deep Learning#Machine Learning#Distributed Systems#Testing
Pratik Dhanave · ·6 min read

Graph Attention Networks — Learning Which Neighbors Matter

The graph convolutional network treats all of a node's neighbors as (near) equals. But that's often wrong — some neighbors are far more relevant than others, and which ones depends on the context. Graph attention networks fix this by letting the model learn how much to weigh each neighbor, bringing the attention mechanism that revolutionized language models to graphs. This post explains attention on graphs and why it's so often the right upgrade over plain averaging.

The GCN treats all neighbors as equal, but some matter far more and which ones is context-dependent. Graph attention networks learn data-dependent weights so a node listens more to important neighbors — the transformer's attention mechanism adapted to graphs — with interpretability as a bonus. When it helps and the honest trade-offs.

#Graph Neural Networks#Attention#GAT#Deep Learning#Machine Learning
Pratik Dhanave · ·6 min read

GraphSAGE — Inductive Learning and Scaling to Big Graphs

The graph convolutional network has two problems that make it impractical for real systems: it can't handle new nodes, and it needs the whole graph in memory at once. GraphSAGE fixes both with one shift in perspective — learn a reusable function that builds a node's embedding from a sample of its neighbors, rather than learning a fixed embedding per node. This is the idea that made GNNs usable on billion-node industrial graphs, and it's why most production GNNs are GraphSAGE-style.

The GCN can't handle new nodes and needs the whole graph in memory. GraphSAGE fixes both: learn a reusable function that builds an embedding from a sample of neighbors, rather than a fixed per-node vector. Inductive learning and neighbor sampling — the ideas that made GNNs work on billion-node industrial graphs.

#Graph Neural Networks#GraphSAGE#Inductive Learning#Scalability#Deep Learning#Machine Learning#Distributed Systems#Embeddings
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
Pratik Dhanave · ·5 min read

Message Passing — The Core Mechanism of Every GNN

Strip away the variations and every graph neural network does the same fundamental thing: each node repeatedly gathers information from its neighbors and updates itself. This is message passing, and it's the single idea you need to understand GNNs — GCN, GraphSAGE, GAT, and the rest are all just different choices for how a node listens to its neighbors. This post builds message passing from scratch and shows why it elegantly satisfies everything a graph architecture must.

Strip away the variations and every GNN does the same thing: each node repeatedly aggregates information from its neighbors and updates itself. Message passing, why stacking layers widens a node's view to its k-hop neighborhood (convolution generalized to graphs), and the failure modes (over-smoothing, over-squashing) that shape real GNNs.

#Graph Neural Networks#Message Passing#Deep Learning#Fundamentals#Machine Learning
Pratik Dhanave · ·6 min read

Node Embeddings and Graph Learning Tasks

Before diving into how graph neural networks work, it's worth being precise about what they produce and what problems they solve. The output is embeddings — vectors that place each node (or edge, or whole graph) in a meaningful space — and the problems come in three flavors that cover almost every graph application. Getting this vocabulary straight makes the architectures in the rest of the series click into place.

A GNN produces embeddings that fuse what a node is (features) with where it sits (structure), and graph problems come in three levels: node-level (classification), edge-level (link prediction — recommendation, knowledge-graph completion), and graph-level (molecule classification). Plus the decisive transductive vs inductive distinction.

#Graph Neural Networks#Embeddings#Link Prediction#Machine Learning#Deep Learning
Pratik Dhanave · ·6 min read

Why Graphs Need Their Own Deep Learning

Neural networks conquered images, text, and audio — but a huge amount of the world's most valuable data isn't any of those. It's graphs: social networks, molecules, knowledge graphs, payment networks, supply chains. Standard deep learning can't handle them, because graphs break the assumptions CNNs and transformers are built on. Graph neural networks are the architecture built for data whose meaning lives in its connections, and this series builds them from first principles.

Neural networks conquered images, text, and audio — but much of the world's most valuable data is graphs: social networks, molecules, knowledge graphs, payment networks. Why standard deep learning can't handle them, the two properties a graph architecture must have, and what a GNN learns instead.

#Graph Neural Networks#Deep Learning#Fundamentals#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.