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Can Modifying Data Address Graph Domain Adaptation?
Graph neural networks (GNNs) have demonstrated remarkable success in numerous graph
analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to …
analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to …
Extracting Training Data from Molecular Pre-trained Models
Abstract Graph Neural Networks (GNNs) have significantly advanced the field of drug
discovery, enhancing the speed and efficiency of molecular identification. However, training …
discovery, enhancing the speed and efficiency of molecular identification. However, training …
BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations
Brain network analysis is vital for understanding the neural interactions regarding brain
structures and functions, and identifying potential biomarkers for clinical phenotypes …
structures and functions, and identifying potential biomarkers for clinical phenotypes …
[PDF][PDF] Enhancing Cross-domain Link Prediction via Evolution Process Modeling
X Huang, W Chow, Y Zhu, Y Wang, Z Chai… - THE WEB …, 2025 - yangy.org
Dynamic graphs are widespread in the real world [5, 47], their nodes representing entities
and dynamic edges denoting complex interactions between them [20]. For example, in …
and dynamic edges denoting complex interactions between them [20]. For example, in …
A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective
Graph self-supervised learning (SSL) is now a go-to method for pre-training graph
foundation models (GFMs). There is a wide variety of knowledge patterns embedded in the …
foundation models (GFMs). There is a wide variety of knowledge patterns embedded in the …