4 papers
LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection
Hang Gao, Xiaoyu Chen, Baoquan Cui +4
Malicious Python packages have become a major threat to software supply chain ecosystems due to the widespread adoption of open-source repositories such as PyPI. Existing learning-…
Efficient Code Analysis via Graph Representation Learning-Guided Large Language Models
Hang Gao, Tao Peng, Baoquan Cui +4
Large Language Models (LLMs) have significantly advanced code analysis tasks, yet they struggle to detect malicious behaviors fragmented across files, whose intricate dependencies…
A Closer Look at the Application of Causal Inference in Graph Representation Learning
Hang Gao, Kunyu Li, Huang Hong +2
Modeling causal relationships in graph representation learning remains a fundamental challenge. Existing approaches often draw on theories and methods from causal inference to iden…
Heterogeneous Attributed Graph Learning via Neighborhood-Aware Star Kernels
Hong Huang, Chengyu Yao, Haiming Chen +1
Attributed graphs, typically characterized by irregular topologies and a mix of numerical and categorical attributes, are ubiquitous in diverse domains such as social networks, bio…