Publications

Book

“Xingquan Zhu, Haicheng Tao, Zhiang Wu, Jie Cao, Kristopher Kalish, and Jeremy Kayne, Fraud Prevention in Online Digital Advertising, SpringerBriefs in Computer Science, ISBN 978-3-319-56793-8, June 2017.”

Conferences

• Min Shi, Yufei Tang, Xingquan Zhu, David Wilson, Jianxun Liu, Multi-Class Imbalanced Graph Convolutional Network Learning, In Proc. of the 29th International Joint Conference on Artificial Intelligence (IJCAI-2020), Yokohama, Japan, July 11-17, 2020.
• Man Wu, Shirui Pan, Chuan Zhou, Xiaojun Chang, Xingquan Zhu, Unsupervised Domain Adaptive Graph Convolutional Networks. In Proc. of the 29th World Wide Web Conference (WWW-2020), Taipei, Taiwan, April 20-24, 2020. 
• Anak Wannaphaschaiyong, Xingquan Zhu, COPD Disease Classification using Network Embedding with Synthetic Relationships, In Proc. of the 33rd International FLAIRS (Florida Artificial Intelligence Research Society) Conference (FLAIRS-2020), North Miami Beach, Florida, USA, May 17-20, 2020.
• Yuping Su , Xingquan Zhu , Bei Dong , Yumei Zhang,  Xiaojun Wu, MedFroDetect: Medicare Fraud Detection with Extremely Imbalanced Class Distributions. In Proc. of the 33rd International FLAIRS (Florida Artificial Intelligence Research Society) Conference (FLAIRS-2020), North Miami Beach, Florida, USA, May 17-20, 2020. 
• Man Wu, Shirui Pan, Xingquan Zhu, Chuan Zhou, Lei Pan, Domain-Adversarial Graph Neural Networks for Text Classification, In Proc. of the 19th IEEE International Conference on Data Mining (ICDM-2019), Beijing, China, Nov 8-11, 2019.
• Shichao Zhu, Chuan Zhou, Shirui Pan, Xingquan Zhu, and Bin Wang, Relation Structure-Aware Heterogeneous Graph Neural Network, In Proc. of the 19th IEEE International Conference on Data Mining (ICDM-2019), Beijing, China, Nov 8-11, 2019.
• Man Wu, Shirui Pan, Lan Du, Ivor Tsang, Xingquan Zhu, Bo Du, Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning, In Proc. of the 28th ACM International Conference on Information and Knowledge Management (CIKM-2019), Beijing, China, Nov 3-7, 2019.
• Magdalyn Elkin, Whitney A.J. Andrews, Xingquan Zhu. Network Analysis and Recommendation for Infectious Disease Clinical Trial Research. In Proc. of the 10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB-2019), Niagara Falls, New York, September 7-10, 2019.
• Ting Guo, Xingquan Zhu, Yang Wang, Fang Chen. Discriminative Sample Generation for Deep Imbalanced Learning. In Proc. of the 28th International Joint Conference on Artificial Intelligence (IJCAI-2019), Macao, China, August 10-16, 2019.
• Huimei Han , Xingquan Zhu , Ying Li.  EDLT: Enabling Deep Learning for Generic Data Classification. In Proc. of the 18th IEEE International Conference on Data Mining ( ICDM- 2018 ), pp. 147-156, November 17-20, Singapore, 2018.
• Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang. SINE: Scalable Incomplete Network Embedding. In Proc. of the 18th IEEE International Conference on Data Mining ( ICDM-2018 ), pp.737-746, November 17-20, Singapore, 2018. 
• Haibo Wang, Chuan Zhou, Jia Wu, Weizhen Dang, Xingquan Zhu, Jilong Wang. Deep Structure Learning for Fraud Detection. In Proc. of the 18th IEEE International Conference on Data Mining ( ICDM-2018), pp.567-576, November 17-20, Singapore, 2018. 

Journal

• Min Shi, Yufei Tang, Xingquan Zhu, JianxunLiu, Topic-aware Web Service Representation Learning.  ACM Transactions on the Web  , 14(2): 9:1-9:23 (2020)
• Huimei Han, Xingquan Zhu, Ying Li, Generalizing Long Short-Term Memory Network for Deep Learning from Generic Data,  ACM Transactions on Knowledge Discovery from Data  , 13:1-13:28, 2020.
• Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang, Network Representation Learning: A Survey,  IEEE Transactions on Big Data  , 6(1):3-28, 2020.
• Min Shi, Yufei Tang, Xingquan Zhu, Jianxun Liu, Haibo He, Topical Network Embedding,  Data Mining and Knowledge Discovery  , 34(1):75-100, 2020.
• Min Shi, Yufei Tang, Xingquan Zhu, MLNE: Multi-Label Network Embedding,   IEEE Trans. on Neural Networks and Learning Systems  , Accepted, In press, 2019 (DOI: 10.1109/TNNLS.2019.2945869)
• Christian Garbin, Xingquan Zhu, Oge Marques, Dropout vs. batch normalization: an empirical study of their impact to deep learning.  Multimedia Tools and Applications   (2020). https://doi.org/10.1007/s11042-019-08453-9
• Jorge Agnese, Jonathan Herrera, Haicheng Tao, Xingquan Zhu, A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis,  WIREs Data Mining Knowledge Discovery  , Accepted, In press, 2019.
• Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang. Attributed Network Embedding via Subspace Discovery,  Data Mining and Knowledge Discovery , 33(6):1953-1980, 2019.
• Huimei Han, Ying Li, Xingquan Zhu. Convolutional Neural Network Learning for Generic Data Classification,  Information Sciences , vol.477, pp.448-465, March 2019.