35 skills found · Page 1 of 2
pygda-team / PygdaPyGDA is a Python library for Graph Domain Adaptation
Evgeneus / Graph Domain AdaptaionPyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021)
xmed-lab / GraphEchoICCV 2023, "GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation"
Skyorca / Awesome Graph Domain Adaptation PapersPublished papers focusing on graph domain adaptation, with survey paper online as Domain Adaptation for Graph Representation Learning: Challenges, Progress, and Prospects
Wang-ML-Lab / GRDA[ICLR 2022] Graph-Relational Domain Adaptation
luo-junyu / GALA[TPAMI24] GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation
Skyorca / OpenGDA(CIKM'23 resource track) Graph Domain Adaptation Benchmark for Cross-network Learning
Luoyadan / SF PGLThis work is the official Pytorch implementation of our papers: Source-Free Progressive Graph Learning for Open-Set Domain Adaptation (TPAMI))
daiquanyu / AdaGCN TKDEThis paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel graph transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains.
mancinimassimiliano / AdagraphPyTorch implementation of AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs
Shen-Lab / GDA SpecReg[ICLR 2023] "Graph Domain Adaptation via Theory-Grounded Spectral Regularization" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen
Graph-COM / StruRW[ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)
luo-junyu / ROSEROSE: Robust Cross Supervision with Neighborhood Mining for Source-free Graph Domain Adaptation
LukasHedegaard / DageOfficial TensorFlow implementation for "Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol" [TIP 2021] and "Supervised Domain Adaptation using Graph Embedding" [ICPR 2020]
Meihan-Liu / 24AAAI A2GNNRethinking Propagation for Unsupervised Graph Domain Adaptation (AAAI-24)
CrownX / SPAOfficial implementation for SPA: A Graph Spectral Alignment Perspective for Domain Adaptation (NeurIPS 2023)
rynewu224 / GraphDAUnsupervised Domain Adaptation on Graphs
firojalam / Domain AdaptationDomain Adaptation with Adversarial Training and Graph Embeddings
Graph-COM / Pair Align[ICML 2024] Code for Pairwise Alignment Improves Graph Domain Adaptation (Pair-Align)
a791702141 / SSGThis project is the official implementation of ``Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation'' in PyTorch, which is accepted by ACM MM 2022.
