5 citations · 6 across the 6 of their papers we have counts for
6 papers
Unlearning Inversion Attacks for Graph Neural Networks
Jiahao Zhang, Yilong Wang, Zhiwei Zhang +2
Graph unlearning methods aim to efficiently remove the impact of sensitive data from trained GNNs without full retraining, assuming that deleted information cannot be recovered. In…
Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks
Fangtong Zhou, Xiaorui Liu, Ruozhou Yu +1
Traffic Engineering (TE) in large-scale networks like cloud Wide Area Networks (WANs) and Low Earth Orbit (LEO) satellite constellations is a critical challenge. Although learning-…
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
Rui Xue, Tong Zhao, Neil Shah +1
Graph neural networks (GNNs) have demonstrated remarkable success in graph representation learning, and various sampling approaches have been proposed to scale GNNs to applications…
Graph Machine Learning in the Era of Large Language Models (LLMs)
Shijie Wang, Jiani Huang, Zhikai Chen +8
Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep lear…
Linear-Time Graph Neural Networks for Scalable Recommendations
Jiahao Zhang, Rui Xue, Wenqi Fan +4
In an era of information explosion, recommender systems are vital tools to deliver personalized recommendations for users. The key of recommender systems is to forecast users' futu…
Efficient End-to-end Language Model Fine-tuning on Graphs
Rui Xue, Xipeng Shen, Ruozhou Yu +1
Learning from Text-Attributed Graphs (TAGs) has attracted significant attention due to its wide range of real-world applications. The rapid evolution of language models (LMs) has r…