However, a comprehensive survey of this emerging topic is still lacking. Therefore, we aim to provide a comprehensive review of directed graph learning, with a particular focus on a data-centric ...

Dir-GNN is a machine learning model that enables learning on directed graphs. This repository contains the official implementation of the paper "Edge Directionality Improves Learning on Heterophilic ...

This paper presents a graph cut approach to the image segmentation task. Considering the image to be a directed graph with two nodes representing ... Medical Imaging Meets Machine Learning course.

Abstract: In machine learning applications, the data are often high-dimensional and intrinsically related. It is often of interest finding the underlying structure and the causal relationships of the ...

Abstract: Graph machine learning techniques and notably graph neural networks ... Spectral graph convolutional networks (GCNs), however, seem to encounter shortfalls when it comes to directed graphs.

In project management, directed acyclic graphs are used to plan and schedule work. In machine learning, DAGs are used to map neural networks so they can be optimized. Some cryptocurrencies use ...