IEEE

EasyDGL: Encode, Train and Interpret for Continuous-Time Dynamic Graph Learning

Abstract: Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics in continuous time domain for its flexibility. This paper aims to design an ...
IEEE

Graph Convolution for Large-Scale Graph Node Classification Task Based on Spatial and Frequency Domain Fusion

Abstract: In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-based tasks. However, they still face challenges in complex scenarios, particularly in integrating ...
San Francisco Fed

Supply- and Demand-Driven PCE Inflation

Supply- and Demand-Driven PCE Inflation updates data on the contributions to personal consumption expenditures (PCE) inflation from supply-driven versus demand-driven components. This tool is intended ...
GitHub

Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming (NeurIPS 2025)

This is a PyTorch implementation of the GraphRTA algorithm, which tries to address the open-set graph domain adaptation problem, where the goal is to not only correctly classify target nodes into the ...
Frontiers

DWGCN: Distance-Weighted Graph Convolutional Network for Robust Spatial Domain Identification in Spatial Transcriptomics

1 Department of Statistical Science, School of Mathematics, Sun Yat-sen University, Guangzhou, China 2 Center for Biomedical Data Science, Duke-NUS, Singapore, Singapore The final, formatted version ...
GitHub

T-Mamba: A Unified Framework with Long-Range Dependency in Dual-Domain for 2D & 3D Tooth Segmentation

This repository is the official implementation of the T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentation. Currently, T-Mamba supports both 2D and 3D ...