Over 28,000 peer-reviewed scientific papers about graph-powered data science have been published in recent years. But access to the benefits of graph data science is no longer limited to scientists and those with deep pockets. The technology reveals contextual data connections, fuelling intelligent systems and enhancing machine learning predictions.

Google was among the first to use graph-based page ranking to revolutionise search engines. Now graph technology is experiencing exponential growth in usage. Interest in graph data science overlaps with AI and machine learning as companies seek to get the best insights from data.

Graph data science can reason about the ‘shape’ of the connected context for each piece of data through graph algorithms, enabling far superior machine learning modelling. Graph data science lets businesses make predictions in many diverse situations, from fraud detection to tracking customer or patient journeys. It helps companies learn from user journeys to present accurate recommendations for future purchases, supported by evidence from their buying history to build confidence in suggestions. Knowledge graphs are also being put to work to identify new associations between genes and diseases, discovering new drugs.

Analyst firm Gartner has identified graph data science as a key data and analytics technology trend. When asked about the use of AI and machine learning, 92% of companies said the plan to employ graph techniques within five years. Gartner believes a quarter of global Fortune 1000 companies will have built a skills base within three years and will be leveraging graph technologies as part of their data and analytics initiatives. 

Graph technology in central government

Graph technology is being used at the top of government. Data scientists Felisia Loukou and Dr. Matthew Gregory deployed their first machine learning model with the help of graph technology to recommend content to GOV.UK users, based on the page they are visiting.

The scientists explain

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