2 citations · 5 across the 14 of their papers we have counts for
14 papers
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…
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…
AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation
Houcheng Su, Mengzhu Wang, Jiao Li +3
In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its…
GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation
Mengzhu Wang, Jiao Li, Houcheng Su +3
Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing da…
DiM: -Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation
Bingli Wang, Houcheng Su, Nan Yin +2
As a technique to alleviate the pressure of data annotation, semi-supervised learning (SSL) has attracted widespread attention. In the specific domain of medical image segmentation…
Degree-Conscious Spiking Graph for Cross-Domain Adaptation
Yingxu Wang, Mengzhu Wang, Houcheng Su +3
Spiking Graph Networks (SGNs) have demonstrated significant potential in graph classification by emulating brain-inspired neural dynamics to achieve energy-efficient computation. H…
Open-World Test-Time Training: Self-Training with Contrast Learning
Houcheng Su, Mengzhu Wang, Jiao Li +3
Traditional test-time training (TTT) methods, while addressing domain shifts, often assume a consistent class set, limiting their applicability in real-world scenarios characterize…
Continuous Spiking Graph Neural Networks
Nan Yin, Shiqi Fan, Mengzhu Wan +10
Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing contin…
Dynamic Spiking Framework for Graph Neural Networks
Nan Yin, Mengzhu Wang, Zhenghan Chen +3
The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in process…
Singular Value Penalization and Semantic Data Augmentation for Fully Test-Time Adaptation
Houcheng Su, Daixian Liu, Mengzhu Wang +1
Fully test-time adaptation (FTTA) adapts a model that is trained on a source domain to a target domain during the testing phase, where the two domains follow different distribution…