Learning directed acyclic graphs (DAGs) from data is a challenging task both in theory and in practice, because the number of possible DAGs scales superexponentially with the number of nodes. In this ...

Abstract: While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do ...

With the ease provided by current computational programs, medical and scientific journals use bar graphs to describe continuous data. These plots are preferred to represent continuous variables since ...

This repository contains the code supporting the work "Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review". Upon using this repository for ...

Abstract: Representation learning on continuous-time dynamic graphs (CTDGs) is critical for modeling evolving network behaviors. However, existing methods often fail to capture both temporal dynamics ...