GNN stands for Graph Neural Network.
Graph Neural Networks are a type of artificial neural network specifically designed to process and understand data structured as graphs. Graphs are data structures that represent relationships between objects, with nodes representing the objects and edges representing the connections between them.
GNNs are powerful tools for analyzing complex relationships in data, and they have applications in various fields, including:
- Social network analysis: Understanding user interactions, identifying influential individuals, and predicting trends.
- Recommendation systems: Suggesting products, services, or content based on user preferences and connections.
- Drug discovery: Identifying potential drug candidates and understanding how they interact with proteins.
- Computer vision: Analyzing images and videos to understand relationships between objects.
- Natural language processing: Analyzing text to understand relationships between words and sentences.
GNNs work by iteratively updating the representation of each node based on its connections and the features of its neighbors. This process allows the network to learn complex relationships and patterns within the graph.
