Information extraction, drug discovery, and software analysis are just a few applications of this versatile tool.
Graphs are an information-rich way to represent data. A graph consists of nodes — typically represented by circles — and edges — typically represented as line segments between nodes. In a knowledge graph, for instance, the nodes represent entities, and the edges represent relationships between them. In a social graph, the nodes represent people, and an edge indicates that two of those people know each other.
At Amazon Web Services, the use of machine learning (ML) to make the information encoded in graphs more useful to our customers has been a major research focus. In this post, we’ll showcase a variety of graph ML applications that customers have developed in collaboration with AWS scientists, from malicious-account detection and automated document processing to knowledge-graph-assisted drug discovery and protein property prediction.
Introduction to Graph Learning
Graphs can be homogenous, meaning the nodes represent a single type of entity (say, airports), and the edges represent a single type of relationship (say, scheduled flights). Or they can be heterogeneous, meaning they integrate multiple types of relationships among different entities, such as a graph of customers and products connected by both purchase histories and interests, or a knowledge graph of drugs, diseases, genes, and biological pathways connected by relationships such as indication and regulation. Nodes are often associated with data features, such as a product’s price or text description.
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