How do Graph Neural Networks Work?
The core idea behind GNNs is to learn a representation of each node in the graph by aggregating features from its neighbors. This process is repeated iteratively, allowing the model to capture complex patterns and relationships within the graph. The following steps outline the general workflow of a Graph Neural Network:
- Node Embeddings: Each node is initialized with a feature vector, which is then updated based on its neighbors.
- Neighbor Aggregation: The features of neighboring nodes are aggregated using a pooling function, such as mean or sum.
- Node Update: The aggregated features are used to update the node’s representation.
- Graph Pooling: The node representations are pooled to obtain a graph-level representation.
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