17 papers
DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts
Guiquan Sun, Xikun Zhang, Jingchao Ni +1
Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting. Existing CGL approaches typically adopt a task-based formula…
TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection
Wen Shi, Zhe Wang, Huafei Huang +6
Graph Anomaly Detection (GAD) aims to identify atypical graph entities, such as nodes, edges, or substructures, that deviate significantly from the majority. While existing text-ri…
MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs
Yan Sun, Qixin Zhang, Zhiyuan Yu +3
The rapid scaling of large language models~(LLMs) has made inference efficiency a primary bottleneck in the practical deployment. To address this, semi-structured sparsity offers a…
Bridging Sequence and Graph Structure for Epigenetic Age Prediction
Yao Li, Xikun Zhang, Xiaotao Shen +4
Epigenetic clocks based on DNA methylation have emerged as powerful tools for estimating biological age, with broad applications in aging research, age-related disease studies, and…
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…