5 citations · 5 across the 1 of their papers we have counts for
5 papers
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…
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-…
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…
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…
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…