Xiao SHEN is now an Associate Professor at Hainan University. She was a Postdoctoral fellow at The Hong Kong Polytechnic University. Her research interests include graph domain adaptation, cross-network classification, graph neural networks, and graph-based machine learning.
Xiao SHEN received the Ph.D. degree from Department of Computing, The Hong Kong Polytechnic University in 2019, the M.Phil. degree from Department of Computer Science and Technology, University of Cambridge in 2013, and the B.Sc. degree (with First-Class Honours) from Queen Mary University of London and Beijing University of Posts and Telecommunications in 2012.
Xiao SHEN received the Hong Kong PhD Fellowship, PolyU Scholarship for HK PhD Fellowship students, COMP Scholarship for HK PhD Fellowship students, and the Queen Mary Excellent Academic Performance Scholarship.
I am looking for self-motivated master and Ph.D students. Interested students please send me your CV to shenxiaocam@163.com.
PhD in Computer Science, 2019
The Hong Kong Polytechnic University
MPhil in Advanced Computer Science, 2013
University of Cambridge
BSc in e-Commerce Engineering, 2012
Queen Mary University of London & Beijing University of Posts and Telecommunications
Our paper “Open-set Cross-network Node Classification via Unknown-excluded Adversarial Graph Domain Alignment” has been accepted by AAAI 2025. [Paper] [Code]
Our paper “Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification” has been accepted by IEEE Transactions on Neural Networks and Learning Systems. [Paper] [Code]
“The Research on Graph Contrastive Learning and Contrastive Domain Adaptation Methods for Cross-network Node Classification”, National Natural Science Foundation of China, 2024/01-2027/12, PI.
“The Research on Key Technologies of Cross-network Representation Learning based on the Integration of Graph Neural Network and Domain Adaptation”, National Natural Science Foundation of China, 2022/01-2024/12, PI.
“The Research on Key Technologies of Cross-Network Representation Learning for Graph Domain Adaptation”, the Research Start-up Fund of Hainan University, 2021/04-2026/04, PI.
An introduction about our recent IEEE TNNLS paper: “Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification”. [Link]
An introduction about our recent AAAI 2023 paper: “Neighbor Contrastive Learning on Learnable Graph Augmentation”. [Link] , [Video]
An introduction about our papers on cross-network node classification : the CDNE and ACDNE models. [Link]
[1] Xiao Shen, Zhihao Chen, Shirui Pan, Shuang Zhou, Laurence T. Yang, and Xi Zhou*. Open-set Cross-network Node Classification via Unknown-excluded Adversarial Graph Domain Alignment. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI)，pp. 20398-20408, 2025. [Paper] [Code]
[2] Xiao Shen*, Mengqiu Shao, Shirui Pan, Laurence T. Yang, and Xi Zhou. Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification. IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), vol. 35, no. 12, pp. 17842-17855, 2024. [Paper] [Code]
[3] Mengqiu Shao, Peng Xue, Xi Zhou, and Xiao Shen*. Contrastive Domain-adaptive Graph Selective Self-training Network for Cross-network Edge Classification. Pattern Recognition, vol. 152, 110448, 2024. [Paper] [Code]
[4] Xiao Shen, Dewang sun, Shirui Pan, Xi Zhou, and Laurence T. Yang. Neighbor Contrastive Learning on Learnable Graph Augmentation. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), pp. 9782-9791, 2023. [Paper] [Code]
[5] Xiao Shen, Shirui Pan, Kup-Sze Choi, Xi Zhou*. Domain-adaptive Message Passing Graph Neural Network. Neural Networks, vol. 164, pp. 439-454, 2023. [Paper] [Code]
[6] Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao, Xiao Shen*, Dan Wang. Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution. IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 35, no. 5, pp. 4908-4922, 2023. [Paper] [Code]
[7] Xiao Shen, Quanyu Dai*, Sitong Mao, Fu-lai Chung, and Kup-Sze Choi, Network Together: Node Classification via Cross-network Deep Network Embedding, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 32, no. 5, pp. 1935-1948, 2021. [Paper] [Code]
[8] Xiao Shen, Quanyu Dai, Fu-lai Chung, Wei Lu, and Kup-Sze Choi, Adversarial deep network embedding for cross-network node classification, Proceedings of AAAI Conference on Artificial Intelligence (AAAI), pp. 2991-2999, 2020. [Paper] [Code]
[9] Xiao Shen, and Fu-Lai Chung*, Deep Network Embedding for Graph Representation Learning in Signed Networks, IEEE Transactions on Cybernetics (TCyb), vol. 50, no. 4, pp. 1556-1568, 2020. [Paper] [Code]
[10] Xiao Shen, Sitong Mao, and Fu-lai Chung*, Cross-network Learning with Fuzzy Labels for Seed Selection and Graph Sparsification in Influence Maximization, IEEE Transactions on Fuzzy Systems (TFS), vol. 28, no. 9, pp. 2195-2208, 2020.
[11] Xiao Shen, Fu-lai Chung, and Sitong Mao, Leveraging Cross-network Information for Graph Sparsification in Influence Maximization, Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2017.07.
[12] Xiao Shen, and Fu-lai Chung, Deep Network Embedding with Aggregated Proximity Preserving, Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2017.08.
Program Chair (Conference)
• The 7th IEEE International Conference on Data Science and Systems (IEEE DSS-2021)
Program Committee Member (Conference)
• ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD), 2023-2025
• ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2025
• ACM Web Conference (WWW), 2023
• AAAI Conference on Artificial Intelligence (AAAI), 2021-2025
• International Joint Conference on Artificial Intelligence (IJCAI), 2021-2025
Journal Reviewer
• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
• IEEE Transactions on Knowledge and Data Engineering (TKDE)
• ACM Transactions on Knowledge Discovery from Data (TKDD)