Xiaorui Liu

Traffic Engineering in Large-Scale Networks With Generalizable Graph Neural Networks

Zhou, F., Liu, S., Liu, X., Yu, R., & Xue, G. (2026, January 1). IEEE Transactions on Networking, Vol. 1.

Unlearning Inversion Attacks for Graph Neural Networks

Zhang, J., Wang, Y., Zhang, Z., Liu, X., & Wang, S. (2026, February 16).

Efficient End-to-end Language Model Fine-tuning on Graphs

Xue, R., Shen, X., Yu, R., & Liu, X. (2025, August 3).

Exploratory Analysis of the Regulation of Long Non-Coding RNA Transcription with Nucleotide Large Language Models

Wang, W., Hou, Z., Wu, T., Liu, X., & Peng, X. (2025, December 15). (Vol. 12). Vol. 12.

Graph Machine Learning in the Era of Large Language Models (LLMs)

Wang, S., Huang, J., Chen, Z., Song, Y., Tang, W., Mao, H., … Li, Q. (2025, May 6). ACM Transactions on Intelligent Systems and Technology.

A Brief Review of Hemp Fiber Length Measurement Techniques

Green, J., Liu, X., & Yin, R. (2024, October 31). Fibers, Vol. 12.

Adversarial Robustness in Graph Neural Networks: Recent Advances and New Frontier

Hou, Z., Lin, M., Torkamani, M. A., Wang, S., & Liu, X. (2024, October 6). 2024 IEEE 11TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS, DSAA 2024, pp. 433–434.

Data Quality-aware Graph Machine Learning

Wang, Y., Ding, K., Liu, X., Kang, J., Rossi, R., & Derr, T. (2024, October 20). PROCEEDINGS OF THE 33RD ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2024, pp. 5534–5537.

Linear-Time Graph Neural Networks for Scalable Recommendations

Zhang, J., Xue, R., Fan, W., Xu, X., Li, Q., Pei, J., & Liu, X. (2024, May 8).

Manufacturing service capability prediction with Graph Neural Networks

Li, Y., Liu, X., & Starly, B. (2024, April 5). Journal of Manufacturing Systems, Vol. 74, pp. 291–301.

Enhancing Graph Representations Learning with Decorrelated Propagation

Liu, H., Han, H., Jin, W., Liu, X., & Liu, H. (2023, August 4). PROCEEDINGS OF THE 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2023, pp. 1466–1476.

How does the Memorization of Neural Networks Impact Adversarial Robust Models?

Xu, H., Liu, X., Wang, W., Liu, Z., Jain, A. K., & Tang, J. (2023, August 4). PROCEEDINGS OF THE 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2023, pp. 2801–2812.

Large-Scale Graph Neural Networks: The Past and New Frontiers

Xue, R., Han, H., Zhao, T., Shah, N., Tang, J., & Liu, X. (2023, August 4). PROCEEDINGS OF THE 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2023, pp. 5835–5836.

Imbalanced Adversarial Training with Reweighting

Wang, W., Xu, H., Liu, X., Li, Y., Thuraisingham, B., & Tang, J. (2022, November 1). 2022 IEEE International Conference on Data Mining (ICDM), pp. 1209–1214.

Trustworthy AI: A Computational Perspective

Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., … Tang, J. (2022, July 12). ACM Transactions on Intelligent Systems and Technology.