9 papers
An Information-Theoretic Evaluation Framework for Benchmark and Model Diagnosis in Knowledge Tracing
Houru Jiang, Zixi Wang, Tengteng Cheng +6
Knowledge tracing (KT) models are predominantly evaluated using aggregate metrics such as area under the curve (AUC) and accuracy. However, these global scores obscure where the re…
PrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs
Yousef A. Radwan, Yao Li, Qing Qing +5
Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent cycles that add millions of…
CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
Yousef A. Radwan, Yao Li, Qing Qing +5
Biomedical knowledge graphs are increasingly large, dynamic, and multimodal, driven by rapid advances in biotechnology such as high-throughput sequencing. Machine learning models c…
UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning
Danhui Zhang, Zhe Wang, Qing Qing +6
Graph learning research has increasingly shifted toward continual graph learning (CGL), which better reflects real-world scenarios where graphs evolve over time. However, existing…
FairGC: Fairness-aware Graph Condensation
Yihan Gao, Chenxi Huang, Wen Shi +5
Graph condensation (GC) has become a vital strategy for scaling Graph Neural Networks by compressing massive datasets into small, synthetic node sets. While current GC methods effe…
Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification
Yangmei Chen, Zhongyuan Zhang, Xikun Zhang +4
Thyroid nodule classification using ultrasound imaging is essential for early diagnosis and clinical decision-making; however, despite promising performance on in-distribution data…
NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Information
Qing Qing, Huafei Huang, Mingliang Hou +2
Graph anomaly detection (GAD) aims to identify irregular nodes or structures in attributed graphs. Neighbor information, which reflects both structural connectivity and attribute c…
P-MIA: A Profiled-Based Membership Inference Attack on Cognitive Diagnosis Models
Mingliang Hou, Yinuo Wang, Teng Guo +6
Cognitive diagnosis models (CDMs) are pivotal for creating fine-grained learner profiles in modern intelligent education platforms. However, these models are trained on sensitive s…
PrivacyCD: Hierarchical Unlearning for Protecting Student Privacy in Cognitive Diagnosis
Mingliang Hou, Yinuo Wang, Teng Guo +6
The need to remove specific student data from cognitive diagnosis (CD) models has become a pressing requirement, driven by users' growing assertion of their "right to be forgotten"…