27 citations · 32 across the 11 of their papers we have counts for
11 papers
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…
LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
Hang Gao, Wenxuan Huang, Fengge Wu +3
The use of large language models (LLMs) as feature enhancers to optimize node representations, which are then used as inputs for graph neural networks (GNNs), has shown significant…
Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning
Hang Gao, Chenhao Zhang, Tie Wang +4
Large Language Models (LLMs) have achieved remarkable success across various domains. However, they still face significant challenges, including high computational costs for traini…
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach
Hang Gao, Chenhao Zhang, Fengge Wu +3
Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However…
MBDS: A Multi-Body Dynamics Simulation Dataset for Graph Networks Simulators
Sheng Yang, Fengge Wu, Junsuo Zhao
Modeling the structure and events of the physical world constitutes a fundamental objective of neural networks. Among the diverse approaches, Graph Network Simulators (GNS) have em…
Molecular Graph Representation Learning via Structural Similarity Information
Chengyu Yao, Hong Huang, Hang Gao +3
Graph Neural Networks (GNNs) have been widely employed for feature representation learning in molecular graphs. Therefore, it is crucial to enhance the expressiveness of feature re…
Graph Partial Label Learning with Potential Cause Discovering
Hang Gao, Jiaguo Yuan, Jiangmeng Li +4
Graph Neural Networks (GNNs) have garnered widespread attention for their potential to address the challenges posed by graph representation learning, which face complex graph-struc…
Unbiased Image Synthesis via Manifold Guidance in Diffusion Models
Xingzhe Su, Daixi Jia, Fengge Wu +3
Diffusion Models are a potent class of generative models capable of producing high-quality images. However, they often inadvertently favor certain data attributes, undermining the…
Manifold Constraint Regularization for Remote Sensing Image Generation
Xingzhe Su, Changwen Zheng, Wenwen Qiang +4
Generative Adversarial Networks (GANs) have shown notable accomplishments in remote sensing domain. However, this paper reveals that their performance on remote sensing images fall…
A Research and Strategy of Remote Sensing Image Denoising Algorithms
Ling Li, Junxing Hu, Fengge Wu +1
Most raw data download from satellites are useless, resulting in transmission waste, one solution is to process data directly on satellites, then only transmit the processed result…
A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images
Junxing Hu, Ling Li, Yijun Lin +2
In recent years, with the development of aerospace technology, we use more and more images captured by satellites to obtain information. But a large number of useless raw images, l…