Furthermore, the required learning time of the best GNN model was approximately 250–300 times more than that of the best machine-learning model. This outcome suggests that successful and efficient ...

In this paper, we propose a methodology for relationship analysis among variables based on the directed acyclic graphs while identifying the linearity/nonlinearity in their relationships. In general, ...

One way to do so is by inserting the outputs of machine learning model predictions back into the graph. “If your models are good, they’re predicting information that’s equally valuable as part of ...

Following is what you need for this book: This book is for data analysts, graph developers, graph analysts, and graph professionals who want to leverage the information embedded in the connections and ...

More information: Xiaorui Su et al, Interpretable identification of cancer genes across biological networks via ...

This article explores what knowledge graphs are, why they are becoming a favourable data storage format, and discusses their potential to improve artificial intelligence and machine learning ...

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 ...

Her research focuses on developing innovative algorithms and models that push the boundaries of machine learning, optimization, and artificial intelligence. Subscribe To Newsletters 7:08 ...