Understanding, Knowing, and Connecting via Knowledge GraphsKnowledge graphs grant us new and different ways of visualizing our data. The technology connects disparate entities and surfaces the ...

Machine learning on graphs is an important and omnipresent task for a vast variety of applications including anomaly detection and dynamic network analysis. In this paper, a deep generative model is ...

From a machine learning perspective, the development of a recurrent graph neural network for the edge encoder with a suitable attention mechanism may aid model generalization. Additional work is also ...

Click-through rate prediction, which refers to predicting the probability of a user clicking on an ad or item based on input features, is critical in the development of recommendation systems.

Graphs are among the most widely-used data structures in machine learning. Their power comes from the flexibility of capturing relations (edges) of collections of entities (nodes) which arise in a ...

The machine-learning score was better able to predict this outcome (AUC 0.92) than a visual stenosis grade (AUC 0.84; P < 0.001) and on par with FFR CT (AUC 0.93; P = 0.34). Additionally, it had an ...

Omnity also uses a combination of machine learning (ML) and graph processing. Omnity has its own internal database of 15 TB worth of documents, and when users submit documents to be processed it ...