28 citations · 108 across the 31 of their papers we have counts for
11 papers · 1 filter
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Jintang Li, Jiawang Dan, Ruofan Wu +9
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications,…
HeteroNet: Heterophily-aware Representation Learning on Heterogenerous Graphs
Jintang Li, Zheng Wei, Jiawang Dan +9
Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literat…
Self-supervision meets kernel graph neural models: From architecture to augmentations
Jiawang Dan, Ruofan Wu, Yunpeng Liu +8
Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the…
Backpropagation Path Search On Adversarial Transferability
Zhuoer Xu, Zhangxuan Gu, Jianping Zhang +3
Deep neural networks are vulnerable to adversarial examples, dictating the imperativeness to test the model's robustness before deployment. Transfer-based attackers craft adversari…
On the Robustness of Latent Diffusion Models
Jianping Zhang, Zhuoer Xu, Shiwen Cui +3
Latent diffusion models achieve state-of-the-art performance on a variety of generative tasks, such as image synthesis and image editing. However, the robustness of latent diffusio…
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Sheng Tian, Jihai Dong, Jintang Li +7
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally conne…
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
Jintang Li, Sheng Tian, Ruofan Wu +6
The prevalence of large-scale graphs poses great challenges in time and storage for training and deploying graph neural networks (GNNs). Several recent works have explored solution…
Mobile User Interface Element Detection Via Adaptively Prompt Tuning
Zhangxuan Gu, Zhuoer Xu, Haoxing Chen +3
Recent object detection approaches rely on pretrained vision-language models for image-text alignment. However, they fail to detect the Mobile User Interface (MUI) element since it…
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
Jintang Li, Huizhe Zhang, Ruofan Wu +5
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensi…
DiffUTE: Universal Text Editing Diffusion Model
Haoxing Chen, Zhuoer Xu, Zhangxuan Gu +6
Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text an…
DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Risk
Jiafu Wu, Mufeng Yao, Dong Wu +6
Graph representation plays an important role in the field of financial risk control, where the relationship among users can be constructed in a graph manner. In practical scenarios…