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 ...
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 ...
IEEE

Graph-based Meta-Learning and Feature Disentanglement for Domain Generalization Crowd Counting

Existing counting models suffer significant performance degradation when tested on data from unknown scenarios (out-of-distribution data), due to the domain shift problem. In practical applications, ...
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

DeGEM: Out-of-Distribution Node Detection by Graph Energy-based Model

Official implementation for ICLR'25 paper ''Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs''[arxiv], by Yuhan Chen*, Yihong Luo*, Yifan Song, Pengwen ...