IEEE

Comprehensive Mapping of Continuous/Switching Circuits in CCM and DCM to Machine Learning Domain Using Homogeneous Graph Neural Networks

Abstract: This paper proposes a method of transferring physical continuous and switching/converter circuits working in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) to graph ...
IEEE

Frequency Domain-Oriented Complex Graph Neural Networks for Graph Classification

Abstract: Graph neural networks (GNNs) could directly deal with the data of graph structure. Current GNNs are confined to the spatial domain and learn real low-dimensional embeddings in graph ...
GitHub

Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptation (WWW-2025).

This is a PyTorch implementation of the GraphATA algorithm, which tries to address the multi-source domain adaptation problem without accessing the labelled source graph. Unlike previous multi-source ...
GitHub

[NeurIPS 24] Continuous Temporal Domain Generalization

This repository contains the official code and datasets for the paper, "Continuous Temporal Domain Generalization", accepted by NeurIPS 2024. TL;DR: The study proposes a method capable of generating ...