Papers GRAPH DOMAIN ADAPTATION
“GRAPH DOMAIN ADAPTATION” 태그가 달린 논문 59편 · 필터 해제
BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis
Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneity, demographics, and site-specific acquisition protocols. Traditional …
Source-Free Domain AdaptationGRAPH DOMAIN ADAPTATIONSafe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation
Source-free graph domain adaptation (SF-GDA) aims to adapt source-trained graph models to unlabeled target graphs when source graphs are no longer accessible. A central obstacle is pseudo-label reliability: under feature…
GRAPH DOMAIN ADAPTATIONContrastive LearningDSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation
Graph domain adaptation (GDA) aims to transfer knowledge from a labeled source graph to an unlabeled target graph under distribution shifts. However, existing methods are largely feature-centric and overlook structural d…
GRAPH DOMAIN ADAPTATIONDual-branch Graph Domain Adaptation for Cross-scenario Multi-modal Emotion Recognition
Multimodal Emotion Recognition in Conversations (MERC) aims to predict speakers' emotional states in multi-turn dialogues through text, audio, and visual cues. In real-world settings, conversation scenarios differ signif…
Multimodal Emotion RecognitionGRAPH DOMAIN ADAPTATIONDomain GeneralizationFreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting
Accurate forecasting of renewable energy generation is essential for efficient grid management and sustainable power planning. However, traditional supervised models often require access to labeled data from the target s…
GRAPH DOMAIN ADAPTATIONGraph Neural NetworkContinual LearningLearning Adaptive Distribution Alignment with Neural Characteristic Function for Graph Domain Adaptation
Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs but is challenged by complex, multi-faceted distributional shifts. Existing methods attempt to reduce distributional…
GRAPH DOMAIN ADAPTATIONLearning Structure-Semantic Evolution Trajectories for Graph Domain Adaptation
Graph Domain Adaptation (GDA) aims to bridge distribution shifts between domains by transferring knowledge from well-labeled source graphs to given unlabeled target graphs. One promising recent approach addresses graph t…
GRAPH DOMAIN ADAPTATIONUSBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation
SF-GDA is pivotal for privacy-preserving knowledge transfer across graph datasets. Although recent works incorporate structural information, they implicitly condition adaptation on the smoothness priors of sourcetrained …
Computational EfficiencyGRAPH DOMAIN ADAPTATIONGraph Domain Adaptation via Homophily-Agnostic Reconstructing Structure
Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. However, existing GDA methods typically assume that both source and tar…
GRAPH DOMAIN ADAPTATIONDisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
Graph Domain Adaptation (GDA) aims to transfer graph classifiers across domains with both semantic and topological shifts. Existing Euclidean adversarial methods face two challenges: Structural Degeneration, where domain…
Representation LearningGRAPH DOMAIN ADAPTATIONEnhancing Node-Level Graph Domain Adaptation by Alleviating Local Dependency
Recent years have witnessed significant advancements in machine learning methods on graphs. However, transferring knowledge effectively from one graph to another remains a critical challenge. This highlights the need for…
GRAPH DOMAIN ADAPTATIONEmpowering GNNs for Domain Adaptation via Denoising Target Graph
We explore the node classification task in the context of graph domain adaptation, which uses both source and target graph structures along with source labels to enhance the generalization capabilities of Graph Neural Ne…
GRAPH DOMAIN ADAPTATIONNode ClassificationTowards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming
Unsupervised Graph Domain Adaptation has become a promising paradigm for transferring knowledge from a fully labeled source graph to an unlabeled target graph. Existing graph domain adaptation models primarily focus on t…
GRAPH DOMAIN ADAPTATIONRethinking Graph Domain Adaptation: A Spectral Contrastive Perspective
Graph neural networks (GNNs) have achieved remarkable success in various domains, yet they often struggle with domain adaptation due to significant structural distribution shifts and insufficient exploration of transfera…
GRAPH DOMAIN ADAPTATIONContrastive LearningFrom Noisy to Native: LLM-driven Graph Restoration for Test-Time Graph Domain Adaptation
Graph domain adaptation (GDA) has achieved great attention due to its effectiveness in addressing the domain shift between train and test data. A significant bottleneck in existing graph domain adaptation methods is thei…
GRAPH DOMAIN ADAPTATIONReinforcement LearningStructure-Attribute Transformations with Markov Chain Boost Graph Domain Adaptation
Graph domain adaptation has gained significant attention in label-scarce scenarios across different graph domains. Traditional approaches to graph domain adaptation primarily focus on transforming node attributes over ra…
GRAPH DOMAIN ADAPTATIONNode ClassificationNested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning
Graph Domain Adaptation (GDA) facilitates knowledge transfer from labeled source graphs to unlabeled target graphs by learning domain-invariant representations, which is essential in applications such as molecular proper…
Molecular Property PredictionGRAPH DOMAIN ADAPTATIONGDAIP: A Graph-Based Domain Adaptive Framework for Individual Brain Parcellation
Recent deep learning approaches have shown promise in learning such individual brain parcellations from functional magnetic resonance imaging (fMRI). However, most existing methods assume consistent data distributions ac…
GRAPH DOMAIN ADAPTATIONSparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation
Unsupervised Graph Domain Adaptation (UGDA) leverages labeled source domain graphs to achieve effective performance in unlabeled target domains despite distribution shifts. However, existing methods often yield suboptima…
GRAPH DOMAIN ADAPTATIONBotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
Transferring extensive knowledge from relevant social networks has emerged as a promising solution to overcome label scarcity in detecting social bots and other anomalies with GNN-based models. However, effective transfe…
Domain AdaptationGRAPH DOMAIN ADAPTATIONModel OptimizationTransfer Learning