Graph Neural Network (GNN)
A Graph Neural Network (GNN) is a neural network architecture designed to handle data structured as graphs, where nodes represent entities and edges represent relationships. Unlike CNNs or RNNs that deal with grids or sequences, GNNs operate on non-Euclidean data, making them ideal for relational problems.
Background
GNNs emerged as a solution to model structured data such as social networks, molecular graphs, and transportation systems. Their core mechanism is message passing, in which nodes iteratively update their representations by aggregating information from neighboring nodes.
Applications
- Recommendation systems: suggesting movies or products by analyzing user-item graphs.
- Fraud detection: identifying suspicious transactions in financial networks.
- Drug discovery: predicting molecular properties or interactions.
- NLP: modeling syntactic trees and semantic graphs for tasks like machine translation.
Strengths and challenges
- ✅ Excellent for capturing relational and structural dependencies.
- ✅ Strong performance in domains where data is inherently graph-based.
- ❌ Computationally expensive on large graphs.
- ❌ Risk of over-smoothing, where node representations become indistinguishable.
Graph Neural Networks have become a cornerstone in modern machine learning because they handle non-Euclidean data—structures where relationships matter as much as individual entities. Unlike images or text, graphs can be irregular, with each node having a different number of connections. GNNs address this challenge by using message passing, where information flows along edges so that each node gradually builds a contextualized representation of itself.
Beyond social networks and chemistry, GNNs are now central in knowledge graphs, powering search engines and question answering. They are also applied in physics-informed learning, for example in modeling particle interactions or traffic flow dynamics.
Still, GNNs face challenges: scaling to very large graphs often requires sampling techniques (GraphSAGE, Cluster-GCN), and oversmoothing—where node representations become indistinguishable in deep architectures—remains an open research problem. Despite these hurdles, GNNs are widely considered one of the most promising directions for combining deep learning with structured reasoning.
📚 Further Reading
- Zhou, J. et al. (2020). Graph Neural Networks: A Review of Methods and Applications. AI Open.
