IEEE

Spectral Graph Convolutional Neural Networks in the Context of Regularization Theory

Abstract: Graph convolutional neural networks (GCNN) have been widely used in graph learning and related applications. It has been identified that the filters in the state-of-the-art spectral graph ...
IEEE

Graph-Theory-Based Derivation, Modeling, and Control of Power Converter Systems

Abstract: Graph-theoretical approaches have been widely applied in many disciplines, however, their implementation in power electronics converters and systems is still in the exploring stage. In this ...
GitHub

Diffusion Models for Graphs Benefit From Discrete State Spaces

This is the official repository for the paper "Diffusion Models for Graphs Benefit From Discrete State Spaces". Link to the paper: https://arxiv.org/abs/2210.01549 As ...
GitHub

Graph-and-Geometric-Learning/D4Explainer

torch==1.10.1 torch-geometric==2.0.4 numpy==1.24.2 pandas==1.5.3 networkx==3.0 ...