A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

Data models and query languages are admittedly somewhat dry topics for people who are not in the inner circle of connoisseurs. Although graph data models and query languages are no exception to that ...

Graph database developer Neo4j Inc. is upping its machine learning game today with a new release of Neo4j for Graph Data Science framework that leverages deep learning and graph convolutional neural ...

By leveraging a combination of technologies revolving around cloud computing, knowledge graphs, machine learning, and Natural Language Processing (NLP), organizations can automatically map the most ...

If open source is the new normal in enterprise software, then that certainly holds for databases, too. In that line of thinking, Github is where it all happens. So to have been favorited 10.000 times ...

Graph machine learning (or graph model), represented by graph neural networks, employs machine learning (especially deep learning) to graph data and is an important research direction in the ...

Graph analytics platform TigerGraph has just released its new TigerGraph ML Workbench, a Jupyter-based Python development framework. TigerGraph says this machine learning toolkit “enables data ...

How would you feel if you saw demand for your favorite topic — which also happens to be your line of business — grow 1,000% in just two years’ time? Vindicated, overjoyed, and a bit overstretched in ...