14 Apr Machine Learning and Deep Link Graph Analytics: A Powerful Combination
Machine learning (ML) – an aspect of artificial intelligence (AI) that allows software to accurately identify patterns and predict outcomes – has become a hot industry topic. With ever-increasing advances in data analysis, storage, and computing power in the last few years, machine learning has been playing an increasingly important role in enterprise applications such as fraud prevention, personalized recommendation, predictive analytics, and so on.
Applying graph database capabilities to ML and AI apps is relatively new, however. That’s surprising in light of the fact that Google’s Knowledge Graph, which first popularized the concept of finding relationships within data to yield more relevant and precise information, dates back to 2012. Also, it’s a natural fit: Graphs are ideal for storing, connecting, and making inferences from complex data.
The main reason that graphs have not played an important role in ML is that legacy graph databases cannot deliver what is really needed for machine learning: deep link graph analytics for large datasets.
Let’s take a deeper dive into how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.
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