Graph Machine Learning
Machine learning is a subset of artificial intelligence that aims to provide systems with the ability to learn and improve from data. It has achieved impressive results in many different applications, especially where it is difficult or unfeasible to explicitly define rules to solve a specific task. For instance, we can train algorithms to recognize spam emails, translate sentences into other languages, recognize objects in an image, and so on.
In recent years, there has been an increasing interest in applying machine learning to graph-structured data. Graphs, composed of nodes and edges, naturally represent relationships and interactions in many real-world systems, making them a better choice in many scenarios where “traditional” machine learning models may overlook these important dependencies. For example, graph machine learning has found wide applications in recommendation systems, where the relationships between users and products (e.g...
