Combining graphs and machine learning has been getting a lot of attention lately, especially since the work published by researchers from DeepMind, Google Brain, MIT, and the University of Edinburgh.

Machine learning is great for answering questions, and knowledge graphs are a step towards enabling machines to more deeply understand data such as video, audio and text that don’t fit neatly ...

ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering. Written by George Anadiotis, Contributor Aug. 27, 2020 at 5:00 a.m. PT.

Graph embeddings make this possible by sampling the topology and properties of the graph to reduce its complexity to just the significant features that are needed for further machine learning ...

A s 2022 dawns, knowledge graphs bear the dubious distinction of being at the epicenter of AI and machine learning for two reasons. One is that, unassisted, they are one of the myriad manifestations ...

The cloud’s place in the data environment is growing, and TigerGraph wants to bolster its role. Today, the company rolled out several new features so cloud users can deliver more analytics and ...

More information: Xiaorui Su et al, Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning, Nature Biomedical Engineering (2025).

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Bringing knowledge graph and machine learning technology together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. With knowledge graphs, AI language ...