Graph machine learning is a type of machine learning that involves using graph data structures to represent and analyze relationships between data points. A graph consists of a set of nodes (also called vertices) and edges that connect the nodes. Each node represents a data point, and the edges represent the relationships or connections between the data points.
In graph machine learning, algorithms are used to analyze the structure and patterns in the graph data in order to make predictions or recommendations. Graph machine learning can be used for a wide range of applications, including recommendation systems, fraud detection, and social network analysis.
Some common techniques used in graph machine learning include:
- Graph neural networks: Graph neural networks are a type of neural network that is specifically designed to process graph data. They can be used to learn patterns and relationships in the graph data and make predictions based on those patterns.
- Graph embeddings: Graph embeddings are a way of representing graph data in a lower-dimensional space, making it easier to analyze and visualize. Embeddings can be learned using techniques such as matrix factorization or deep learning.
- Random walks: Random walks are a way of exploring the graph data by starting at a random node and then following the edges to other nodes. The sequence of nodes visited during the random walk can be used to learn patterns in the graph data.
Overall, graph machine learning is a powerful tool for analyzing and understanding relationships between data points in complex systems. It can be used to uncover hidden patterns and make predictions that would be difficult to discover using traditional machine learning techniques.