Graph Learning

Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. It leverages the relationships and structure within graphs to learn and predict, including techniques such as graph neural networks, which can capture dependencies and influences between connected nodes, thereby enhancing prediction accuracy. Key application areas of graph learning encompass recommendation systems, drug discovery, social network analysis, and fraud detection. By mining the intrinsic structure of graph data, graph learning reveals deep insights and patterns that are difficult to uncover with traditional methods.