Research Associate, School of Computing, Macquarie University, Sydney, NSW 2109, Australia Email: xuexiong.luo@mq.edu.au [GitHub] [Google Scholar]

About me

I'm a research associate worked with A/Prof. Jia Wu at School of Computing, Macquarie University. I studied at Macquarie Univeristy for my PhD advised by A/Prof. Jia Wu and Prof. Jian Yang. Research interests: Graph Neural Networks, Graph Anomaly Detection, Brain Network Analysis.

News

• I was awarded Vice-Chancellor's Commendation for postgraduate research based on my PhD research work.
  

• I was invited as the Reviewer in Nature Communications Journal.
  

• Our paper "Multi-View Graph Anomaly Detection via Subgraph Anomaly Augmentation " has been accepted by Neurocomputing.
  

• I was invited as PC Member in top AI conference IJCAI 2025 (CCF A).
  

• Our paper "ReiPool: Reinforced Pooling Graph Neural Networks for Graph-Level Representation Learning " has been accepted by TKDE.
  

• I was invited as Area Chair in top neural network conference IJCNN 2025 (CCF C).
  

• Our paper "Counterfactual Brain Graph Augmentation Guided Bi-Level Contrastive Learning for Disorder Analysis " has been accepted by ICDM 2024.
  

• Our paper "Uncertainty Graph Convolution Recurrent Neural Network for Air Quality Forecasting " has been accepted by Advanced Engineering Informatics Journal.
  

• I got the Student Travel Grant of IJCAI 2024 to attend the IJCAI conference in Jeju.
  

• I was awarded the HDR Research Rising Star Award by the School of Computing at Macquarie University.
  

• Our paper "Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning " has been accepted by SSDBM 2024.
  

• Our paper "Anomaly-Aware Variational Graph Autoencoder Based Graph-Level Anomaly Detection Algorithm " has been accepted by Journal Computer Research and Development（CCF A Chinese Journal）
  

• Our paper "Graph Neural Networks for Brain Graph Learning: A Survey " has been accepted by IJCAI 2024.
  

• Our paper "Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration" has been accepted by IEEE TNNLS Journal.
  

• Our paper "An Interpretable Brain Graph Contrastive Learning Framework for Brain Disorder Analysis" has been accepted by WSDM 2024.
  

Publications

• Counterfactual Brain Graph Augmentation Guided Bi-Level Contrastive Learning for Disorder Analysis
  Guangwei Dong, Xuexiong Luo, Jing Du, Jia Wu, Shan Xue, Jian Yang, and Amin Beheshti
  IEEE International Conference on Data Mining (ICDM), 2024.

• ReiPool: Reinforced Pooling Graph Neural Networks for Graph-Level Representation Learning
  Xuexiong Luo, Sheng Zhang, Jia Wu, Hongyang Chen, Hao Peng, Chuan Zhou, Zhao Li, Shan Xue, and Jian Yang
  IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024.

• Uncertainty graph convolution recurrent neural network for air quality forecasting
  Mei Dong, Yue Sun, Yutao Jin, Chen Song, Xiankun Zhang, and Xuexiong Luo
  Advanced Engineering Informatics, 2024.

• Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning
  Zitong Wang, Xuexiong Luo*, Enfeng Song, Qiuqing Bai, and Fu Lin
  Proceedings of the 36th International Conference on Scientific and Statistical Database Management (SSDBM), 2024.

• Graph Neural Networks for Brain Graph Learning: A Survey
  Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Quan Z. Sheng, David McAlpine, Paul Sowman, Alexis Giral and Philip S. Yu
  International Joint Conference on Artificial Intelligence(IJCAI), 2024.

• An Interpretable Brain Graph Contrastive Learning Framework for Brain Disorder Analysis
  Xuexiong Luo, Guangwei Dong, Jia Wu, Amin Beheshti, Jian Yang, and Shan Xue.
  ACM International Conference on Web Search and Data Mining(WSDM), 2024.

• Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration
  Xuexiong Luo, Jia Wu, Jian Yang, Hongyang Chen, Zhao Li, Hao Peng, and Chuan Zhou.
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024.

• Discriminative Graph-Level Anomaly Detection via Dual-Students-Teacher Model
  Fu Lin, Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Zitong Wang and Haonan Gong
  International Conference on Advanced Data Mining and Applications (ADMA), 2023.

• Multi-representations Space Separation based Graph-level Anomaly-aware Detection
  Fu Lin, Haonan Gong, Mingkang Li, Zitong Wang, Yue Zhang and Xuexiong Luo
  Proceedings of the 35th International Conference on Scientific and Statistical Database Management (SSDBM), 2023.

• Reinforcement Learning Guided Multi-Objective Exam Paper Generation [code]
  Yuhu Shang, Xuexiong Luo*, Lihong Wang, Hao Peng, Xiankun Zhang, Yimeng Ren and Kun Liang
  SIAM International Conference on Data Mining (SDM), 2023.

• Deep graph level anomaly detection with contrastive learning [code]
  Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Hao Peng, Chuan Zhou, Hongyang Chen, Zhao Li and Quan Z. Sheng
  Scientific Reports, 2022.

• ComGA: Community-Aware Attributed Graph Anomaly Detection [code]
  Xuexiong Luo, Jia Wu, Amin Beheshti, Jian Yang, Xiankun Zhang, Yuan Wang and Shan Xue
  ACM International Conference on Web Search and Data Mining (WSDM), 2022.

• Adaptive Capsule Network
  Jianwei Tao, Xiankun Zhang, Xuexiong Luo, Yuan Wang, Chen Song and Yue Sun
  Computer Vision and Image Understanding (CVIU), 2022.

• Deep Semantic Network Representation [code]
  Xuexiong Luo, Jia Wu, Chuan Zhou, Xiankun Zhang and Yuan Wang
  IEEE International Conference on Data Mining (ICDM), 2020.

Awards

• Vice-Chancellor's Commendation for postgraduate research award.
  

• Student Travel Grant of IJCAI 2024.
  

• HDR Research Rising Star Award by the School of Computing at Macquarie University.
  

• International Macquarie University Research Excellence Scholarship, 2021, Macquarie University, Sydney, Australia.
  

• National Scholarship, 2020, Tianjin, China.
  

• National Encouragement Scholarship, 2017, Nanchang, China.
  

• The Third Prize in Huawei Cup 16th China Postgraduate Mathematical Contest in Modeling, 2019, China.
  

• Outstanding Master's Thesis of TUST, 2022.
  

Professional Activities

• Journal Reviewer
  Knowledge-Based Systems
  Neurocomputing
  Information Sciences
  Neural Computing and Applications
  Physica A
  Engineering Applications of Artificial Intelligence
  IEEE Transactions on Neural Networks and Learning Systems
  International Journal of Machine Learning and Cybernetics
  Neural Networks
  International Journal of Data Mining and Bioinformatics
  IEEE Transactions on Cybernetics
  Cluster Computing
  ACM Transactions on Knowledge Discovery from Data
  IEEE Transactions on Knowledge and Data Engineering
  Nature Communications
  

• Conference Reviewer
  Area Chair in IJCNN 2025
  PC member in IJCAI, WWW 2025
  ACML 2024, 2025
  MICCAI 2024
  KDD 2024, 2025
  ECAI 2023
  IJCNN 2023
  WWW 2023
  ICDM 2022
  AAAI 2022