Real-World Applications of Graph Neural Networks
We have already seen the working of GNNs in drug discovery and recommendation systems. The following sections present a few more applications:
- Social network analysis: Graph Neural Networks can make it possible to do a social network analysis to extract important users, suggest possible connections, and detect communities of like-minded interests.
- Fraud Detection : Financial transactions are modeled as graphs where GNNs are used to learn features spanning characteristics of users’ behavior and relationship-network features to pick out fraudulent activities from the network of behaviors and relationships.
- Traffic prediction: One can predict the flow of transportation network traffic by considering the connectivity of roads and considering historical as well as real-time sensor information using GNNs.
- Knowledge Graph Completion: GNNs can predict relations or entities missing from a given knowledge graph, by which overall completeness and correctness in a knowledge base can be improved.
- Protein-Protein Interaction Prediction: GNNs can be exploited in the analysis of a network of protein-protein interactions, allowing identification of potential drug targets or research into cellular processes.
Graph Neural Networks: An In-Depth Introduction and Practical Applications
Graph Neural Networks (GNNs) are a class of artificial neural networks designed to process data that can be represented as graphs. Unlike traditional neural networks that operate on Euclidean data (like images or text), GNNs are tailored to handle non-Euclidean data structures, making them highly versatile for various applications. This article provides an introduction to GNNs, their architecture, and practical examples of their use.
Table of Content
- What is a Graph?
- Key Concepts in Graph Neural Networks
- Why do we need Graph Neural Networks?
- How do Graph Neural Networks Work?
- Popular Graph Neural Networks Models
- Training Graph Neural Networks : Implementation
- Benefits and Limitations of GNNs
- Real-World Applications of Graph Neural Networks
- Future Aspects of GNNs