Tech Xplore

New graph attention network models higher-order relationships in complex graph data

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
IEEE

Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentation

Abstract: Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications where annotations are severely scarce and multimodal ...
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 ...
IEEE

Evidential Graph Contrastive Alignment for Source-Free Blending-Target Domain Adaptation

Abstract: In this paper, we firstly tackle a more realistic Domain Adaptation (DA) setting: Source-Free Blending-Target Domain Adaptation (SF-BTDA), where we can not access to source domain data while ...
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 ...