Definition: Graph neural networks (GNNs) are a type of neural network designed to process and analyze graph-structured data.

Detailed Description:

GNNs are used to learn patterns and relationships within graph-structured data, such as social networks, knowledge graphs, and molecular structures. They consist of nodes and edges, where nodes represent entities and edges represent relationships between them.

GNNs use message passing mechanisms to propagate information between nodes, allowing them to learn the structure and features of the graph. This makes them suitable for tasks like node classification, link prediction, and graph clustering.

Related Terms:

• Neural Networks
• Deep Learning
• Graph Theory
• Node Embedding
• Message Passing

FAQs:

1. What is the difference between GNNs and traditional neural networks?

   • GNNs are specifically designed to process graph-structured data, while traditional neural networks are typically used for processing image or text data.

2. What are some applications of GNNs?

   • GNNs are used in various applications, such as recommendation systems, drug discovery, and social network analysis.

3. What are the challenges of training GNNs?

   • Challenges include handling large and complex graphs, designing effective message passing mechanisms, and dealing with overfitting.