What Graph Neural Network (GNN) Meaning, Applications & Example
A neural network that operates on graph-structured data.
What is a Graph Neural Network (GNN)?
A Graph Neural Network (GNN) is a type of neural network designed to work directly with graph data structures. It is used for tasks where data is represented as graphs, such as social networks, molecular structures, or knowledge graphs. GNNs learn to encode the relationships between nodes and edges in the graph to make predictions about the nodes, edges, or the entire graph.
Components of GNN
- Nodes: Represent the entities or objects in the graph, such as users in a social network or atoms in a molecule.
- Edges: Represent the relationships or interactions between nodes, such as friendships or chemical bonds.
- Message Passing: GNNs use message-passing mechanisms, where nodes exchange information with their neighbors to update their features or representations. This process is repeated across multiple layers to capture higher-level relationships.
Applications of GNN
- Social Network Analysis: Analyzing user interactions, predicting friendships, or recommending new connections based on network structures.
- Chemistry and Biology: Modeling molecular structures for drug discovery or predicting protein interactions by representing molecules as graphs.
- Recommendation Systems: Improving product recommendations by learning relationships between users, products, and ratings in a graph-based format.
Example of GNN
An example of a Graph Neural Network is in drug discovery, where molecules are represented as graphs, with atoms as nodes and chemical bonds as edges. A GNN can predict molecular properties or interactions, helping researchers identify promising drug candidates by learning patterns in the graph structure.