3 citations · 3 across the 6 of their papers we have counts for
7 papers
Exploring Concept Subspace for Self-explainable Text-Attributed Graph Learning
Xiaoxue Han, Libo Zhang, Zining Zhu +1
We introduce Graph Concept Bottleneck (GCB) as a new paradigm for self-explainable text-attributed graph learning. GCB maps graphs into a subspace, concept bottleneck, where each c…
HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction
Pengfei Hu, Fan Ming, Xiaoxue Han +3
Deep learning models have shown promise in reservoir inflow prediction, yet their performance often deteriorates when applied to different reservoirs due to distributional differen…
Adaptive Graph Learning with Transformer for Multi-Reservoir Inflow Prediction
Pengfei Hu, Ming Fan, Xiaoxue Han +5
Reservoir inflow prediction is crucial for water resource management, yet existing approaches mainly focus on single-reservoir models that ignore spatial dependencies among interco…
Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization
Pengfei Hu, Xiaoxue Han, Fei Wang +1
Domain generalization has become a critical challenge in predictive healthcare, where different patient groups exhibit shifting data distributions that degrade model performance. S…
MPLite: Multi-Aspect Pretraining for Mining Clinical Health Records
Eric Yang, Pengfei Hu, Xiaoxue Han +1
The adoption of digital systems in healthcare has resulted in the accumulation of vast electronic health records (EHRs), offering valuable data for machine learning methods to pred…
DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification
Xiaoxue Han, Huzefa Rangwala, Yue Ning
Graph Neural Networks (GNNs) are susceptible to distribution shifts, creating vulnerability and security issues in critical domains. There is a pressing need to enhance the general…
A Topology-aware Graph Coarsening Framework for Continual Graph Learning
Xiaoxue Han, Zhuo Feng, Yue Ning
Continual learning on graphs tackles the problem of training a graph neural network (GNN) where graph data arrive in a streaming fashion and the model tends to forget knowledge fro…