GRAPH DOMAIN ADAPTATION
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Benchmarks
FRANKENSTEIN
Most implemented
Adversarial Deep Network Embedding for Cross-network Node Classification
DANE: Domain Adaptive Network Embedding
BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
GCAL: Adapting Graph Models to Evolving Domain Shifts
PyGDA: A Python Library for Graph Domain Adaptation
Papers
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 ADAPTATION