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Open Source Machine Learning Library for Large Data Graphs Gets Update
Last month StellarGraph.an open source, Python-based machine learning algorithm library for graphs created by data scientists and others at Australian-based research and technology company CSIRO Data 61, got a major update (0.80) with numerous new features.
Aside from various bug fixes, the update adds a number of new algorithms, including Directed GraphSAGE, an Attri2vec algorithm and GAT saliency maps -- support for which is another new feature of the update. Click on the links to find out more about each algorithm.
Other new features include improvements in activations and API support, multithreading improvements and a unified API for classes. "The 0.8.1 release of the library...further simplif[ies] graph machine learning workflows through standardized model APIs and arguments," the developers said in an announcement of the update.
There are also numerous refactoring-focused updates, including a change from using keras to tensorflow.keras and refactoring changes for the Ensemble class.
The project was originally released in June 2018. It focuses on combining entity resolution, predictive modeling and data visualization to "leverage the full graph relationships to enable predictions that are not currently possible with standard techniques."
More information on the project can be found here.
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Becky Nagel serves as vice president of AI for 1105 Media specializing in developing media, events and training for companies around AI and generative AI technology. She also regularly writes and reports on AI news, and is the founding editor of PureAI.com. She's the author of "ChatGPT Prompt 101 Guide for Business Users" and other popular AI resources with a real-world business perspective. She regularly speaks, writes and develops content around AI, generative AI and other business tech. She has a background in Web technology and B2B enterprise technology journalism.