Rui Xue
Rui Xue
Welcome to my personal website! I am Rui Xue, a Ph.D. in the Department of Electrical and Computer Engineering at North Carolina State University (NCSU). I earned my master's degree in Electrical and Computer Engineering from the University of Southern California (USC).
Email: rxue@ncsu.edu Linkedin
Research Interests
Large Scale Machine Learning
Graph Machine Learning
Large Language Models
Data Mining
Recommendation Systems
Signal Processing
Publications
XUE Rui et al. "LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation." ICML, 2023.
XUE Rui* et al. "Linear-Time Graph Neural Networks for Scalable Recommendations." WWW, 2024.
XUE Rui et al. "Efficient End-to-end Language Model Fine-tuning on Graphs." KDD, 2025.
XUE Rui et al. "Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks. " ICML, 2025.
XUE Rui et al. "Large-Scale Graph Neural Networks: The Past and New Frontiers." Tutorial, KDD, 2023. (Website)
XUE Rui et al. "Large-Scale Graph Neural Networks: Navigating the Past and Pioneering New Horizons." Tutorial, AAAI, 2024. (Website)
XUE Rui et al. "Large-Scale Graph Neural Networks: Navigating the Past and Pioneering New Horizons." Tutorial, SDM, 2024.
XUE Rui et al. "The Dynamic Error Analysis of the Current Transformer." 23rd IMEKO TC4 International Symposium on Electrical and Electronic Measurements Promote Industry 4.0, 2019.
(* Co-first Author)
Preprints
XUE Rui et al. "H³GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning." arXiv Preprint.
Research Experiences (Details)
Self Supervised Learning on Graphs
Large-Scale Machine Learning on Graphs
Large Language Models Fine-Tuning on Graphs
Graph Neural Networks for Large Scale Recommendations
Trajectory-Aided Beam Configuration at the Mobile Terminal in LEO Satellite Networks
Theoretical Research of Dynamic Characteristic of Complex Electric Load Based on Signal Processing
Beamformer Design and Doppler Shift Analysis Based on Deep Learning Vehicle Motion Prediction in LEO Satellite Communications
Machine Learning Based Transmit Antenna Selection for Downlink MU-MIMO System
Simulation of Molecular Diffusion Based on Deep Learning
Hierarchical Trees Coding for Image Compression Based on Wavelet Transform
Teaching
Teaching Assistant, Neural Networks. 2025 Spring
Teaching Assistant, Computer Networks. 2024 Fall
Teaching Assistant, Communications Engineering. 2022 Fall, 2023 Fall
Teaching Assistant, Analytical Foundations of Electrical and Computer Engineering. 2023 Spring, 2023 Fall, 2024 Spring
Skills
Python, PyTorch, TensorFlow, MATLAB, C/C++, Julia, VB, Multisim, Pspice, Simulink and Unity 3D.