
Traditional machine learning (ML) workflows have proven effective in a wide variety of use cases, from image classification to fraud detection. However, traditional ML leaves relationships between data points to be inferred by the model, which can limit its ability to fully capture the complex structures within the data. In enterprise environments, where data often spans multiple, interwoven systems—such as customer relations, supply chains, and product life cycles—traditional ML approaches can fall short by missing or oversimplifying relationships that drive critical insights into customer behavior, product interactions, and risk factors. In contrast, graph approaches allow these relationships to be explicitly represented, enabling a more comprehensive analysis of complex networks.
Graph machine learning (Graph ML) offers a new paradigm for handling the complexities of real-world data, which often exists in interconnected networks.