Home News Graph Data Science Turbocharges Machine Learning and Analytics

Graph Data Science Turbocharges Machine Learning and Analytics

by gbaf mag

Neo4j’s Alicia Frame explains how graph data can unearth new connections and make predictions that drive business

Graph data science is no longer a technology only companies such as Google have the AI expertise and resources to use. This powerful and innovative technique can discover connections between data through graph algorithms and embeddings, enabling enhanced machine learning predictions.

Graph data technology is fast becoming mainstream in business. In its June ‘Top 10 Data and Analytics Technology Trends for 2020’ report, Gartner states, “Finding relationships in combinations of diverse data, using graph techniques at scale, will form the foundation of modern data and analytics.” A few months after it released that statement, Gartner surveyed companies about using AI and ML techniques. There, a remarkably high 92% said they plan to employ graph techniques within five years.

Organisations don’t always know how to leverage connected data for use in machine learning models. The ability to rapidly ‘learn’ predictive features of data is significant. Knowledge graphs provide value across domains, including identifying new associations between genes and diseases, discovering new drugs and predicting links between customers and products for better recommendations.

Graph data science in action

Graph data science can revolutionise the way enterprises make predictions in many diverse scenarios, from fraud detection to analysing customer or patient journies, to knowledge graph completion applications like drug discovery. In a drug discovery use case, this means not only identifying possible new associations between genes, diseases, drugs and proteins, but also providing immediate context to assess the relevance or validity of these discoveries. For customer recommendations, it means learning from user journeys to make accurate recommendations for future purchases, while presenting options within their buying history to build confidence in suggestions.

Graphs are being deployed in British government. In a recent GOV.UK blog post, One Graph to rule them all, government data scientists discuss deploying their first machine learning model with the help of graph technology. The resulting model automatically recommends content to GOV.UK users, based upon the page they are visiting.

Graph data science is making natural language processing of a large-scale repository of technical documents detailing repairs more effective at Caterpillar, manufacturer of construction and mining equipment. It recognised there was valuable data housed in more than 27 million documents and set about creating a natural language processing tool to uncover these unseen connections and trends. The resulting graph-based machine learning classification tool learns from the portion of data already tagged with terms such as ‘cause’ or ‘complaint’ to apply to the rest of the data. The system uses WordNet as a lexicographic dictionary to provide definitions for the words, and accesses the Stanford Dependency Parser to parse the text and graphs to find patterns and connections, build hierarchies and add ontologies. Once this is all put together, users can conduct meaningful, data science-enhanced searches.

Another example is New York–Presbyterian Hospital’s analytics team’s use of graph data science to track infections more effectively and take strategic action to contain them. Graph data science offers a flexible way to connect all the dimensions of an event – the what, when and where it happened. New York–Presbyterian Hospital wanted to log every event, from the time a patient was admitted, all of the tests they undergo and their eventual release. The team created a ‘time’ and then a ‘space’ tree to model all the rooms patients could be treated in on-site. This initial model revealed a large number of inter-relationships, but that alone did not meet their goals. An event entity was included to connect the time and location trees. The resulting data model means the analytics team is able to analyse everything that happens in its facilities.

Enhancing customer experience

Graph data science can underpin customer journey improvements in all sectors. Ben Squire, senior data scientist at leading media and marketing services company Meredith, shared his experience with graph data science work, stating that the use of graph algorithms is allowing the transformation of billions of page views into millions of pseudonymous identifiers with rich browsing profiles: “Providing relevant content to online users, even those who don’t authenticate, is essential to our business,” he points out. “Instead of ‘advertising in the dark,’ we now better understand our customers, which translates into significant revenue gains and better-served consumers.”

Graph data science has moved out of academia and into business. Gartner thinks that within three years, a quarter of global Fortune 1000 companies will have built a skills base and will be leveraging graph technologies as part of their data and analytics initiatives. Graph data analytics are set to become a key part of business analytics, underpinning truly predictive insights.

The author is Dr. Alica Frame, Neo4j Lead Product Manager – Data Science


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