Graph machine learning is a class of machine learning methods for modeling and analyzing graph-structured data. Its core objective is to learn the structural relationships and feature representations within the data using information such as nodes, edges, and node or edge attributes. Unlike traditional machine learning, which primarily deals with regular grid data or independent samples, graph machine learning can handle data with complex interconnected relationships, such as social networks, knowledge graphs, molecular structures, transportation networks, and recommender systems.

The development of graph machine learning is built upon multiple fields, including graph theory, network analysis, and machine learning. Early research primarily relied on manually designed graph features and traditional graph algorithms for analysis. With the development of deep learning, researchers began exploring Graph Neural Networks (GNNs), utilizing neural networks to directly learn node representations from graph structures. In 2017, researchers at the University of Amsterdam published a paper... Semi-Supervised Classification with Graph Convolutional Networks The paper proposes the Graph Convolutional Network (GCN), which aggregates neighbor node information through graph convolution operations, improving the semi-supervised learning capability on graph data, and has become one of the important representative research works in the field of graph neural networks.

Modern graph machine learning methods are widely used in tasks such as node classification, link prediction, graph classification, and relation reasoning. With the development of graph neural networks, graph representation learning, and graph-based models, this field has been applied to computational biology, drug discovery, knowledge graphs, recommender systems, cybersecurity, and information retrieval, providing important technical means for analyzing complex relational data.