10 papers
MOLAR: Learning Multimodal Molecular Representations from Noisy Labels
Yingxu Wang, Kunyu Zhang, Nan Yin +2
Motivation: Noisy labels are a common challenge in molecular property prediction because molecular annotations are often obtained from assays, curated databases, or weak annotation…
Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation
Yingxu Wang, Xinwang Liu, Siyang Gao +1
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…
Learning beyond Site Bias for OOD Generalization in Brain Networks
Yingxu Wang, Kunyu Zhang, Yanwu Yang +5
Graph-based learning from functional magnetic resonance imaging (fMRI) has shown strong potential for brain network analysis. However, existing methods often degrade under cross-si…
TRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering
Yingxu Wang, Jiaxin Huang, Mengzhu Wang +1
Multi-hop Knowledge Graph Question Answering (KGQA) requires coherent reasoning across relational paths, yet existing methods often treat each reasoning step independently and fail…
DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation
Yingxu Wang, Kunyu Zhang, Jiaxin Huang +4
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 f…
USBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation
Yingxu Wang, Kunyu Zhang, Mengzhu Wang +2
SF-GDA is pivotal for privacy-preserving knowledge transfer across graph datasets. Although recent works incorporate structural information, they implicitly condition adaptation on…
DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
Yingxu Wang, Xinwang Liu, Mengzhu Wang +2
Graph Domain Adaptation (GDA) aims to transfer graph classifiers across domains with both semantic and topological shifts. Existing Euclidean adversarial methods face two challenge…
ProtoMol: Enhancing Molecular Property Prediction via Prototype-Guided Multimodal Learning
Yingxu Wang, Kunyu Zhang, Jiaxin Huang +3
Multimodal molecular representation learning, which jointly models molecular graphs and their textual descriptions, enhances predictive accuracy and interpretability by enabling mo…
DAMR: Efficient and Adaptive Context-Aware Knowledge Graph Question Answering with LLM-Guided MCTS
Yingxu Wang, Shiqi Fan, Mengzhu Wang +3
Knowledge Graph Question Answering (KGQA) aims to interpret natural language queries and perform structured reasoning over knowledge graphs by leveraging their relational and seman…
Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning
Yingxu Wang, Mengzhu Wang, Zhichao Huang +2
Graph Domain Adaptation (GDA) facilitates knowledge transfer from labeled source graphs to unlabeled target graphs by learning domain-invariant representations, which is essential…