Publications

Selected Publications

Preprint


  1. Shiyu Wang*, Yuanqi Du*, Xiaojie Guo, Bo Pan, Liang Zhao. Controllable Data Generation by Deep Learning: A Review [project page]. arXiv e-prints, arXiv:2207.09542

PUBLISHED


  1. [TPAMI] Xiaojie Guo, Liang Zhao. A Systematic Survey on Deep Generative Models for Graph Generation. IEEE Transactions on Pattern Analysis and Machine Intelligence. To appear soon. [paper].

  2. [NeurIPS 2022] Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, Bill Wuest, Amarda Shehu, Liang Zhao. Multi-objective Deep Data Generation with Correlated Property Control.To appear soon. [project page].

  3. [NeurIPS 2022] Shiyu Wang, Xiaojie Guo, Liang Zhao. Deep Generative Model for Periodic Graphs. Conference on Neural Information Processing Systems (NeurIPS 2022) [project page]. Conference on Neural Information Processing Systems (NeurIPS 2022)

  4. [FnTs] Lingfei Wu*, Yu Chen*, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, and Bo Long (*Equally Contributed). "Graph Neural Networks for Natural Language Processing: A Survey". Foundations and Trends in Machine Learning. [PDF] [Media Coverage]: <AI Era> <Graph_RS> <DL_Graph> <ML_NLP> <AITechTalk>

  5. [KDD 2022] Xiaojie Guo, Qingkai Zeng, Meng Jiang, Yun Xiao, Bo Long, LIngfei Wu. Automatic Controllable Product Copywriting for E-Commerce. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), applied science track (acceptance rate: 25.7%), to appear, Washington DC, Aug 2022

  6. [Springer Book] X Guo, S Wang, L Zhao, "Graph Neural Networks: Graph Transformation", Chapter of Book "Graph Neural Networks: Foundations, Frontiers, and Applications", 251-275, Springer, 2022.

  7. [WWW 2022] Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo and Chuan Shi, "Compact Graph Structure Learning via Mutual Information Compression", In the the 31st conference in the International World Wide Web Conference series. [PDF]

  8. [TNNLS] Xiaojie Guo, Lingfei Wu, and Liang Zhao. Deep Graph Translation. CoRR, IEEE Transactions on Neural Networks and Learning Systems (TNNLS). [paper] [code]

  9. [AAAI/IAAI 2022] Xiaojie Guo, Shugen Wang, Hanqing Zhao, Shiliang Diao, Jiajia Chen, Zhuoye Ding, Zhen He, Jianchao Lv, Yun Xiao, Bo Long, Han Yu and Lingfei Wu, "Intelligent Online Selling Point Extraction for E-Commerce Recommendation", In the Thirty-Sixth AAAI Conference on Artificial Intelligence. [AAAI/IAAI 2022 Award].

  10. [SDM 2022] Yuanqi Du*, Xiaojie Guo*, Amarda Shehu, Liang Zhao. Interpretable Molecular Graph Generation via Monotonic Constraints. In SIAM International Conference on Data Mining (SDM 2022), to appear.

  11. [AAAI 2022] Yuanqi Du*, Xiaojie Guo*, Hengning Cao, Yanfang Ye, Liang Zhao.Disentangled Spatiotemporal Graph Generative Model. In Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), to appear.

  12. [Bioinformatics Advances] Xiaojie Guo, Sivani Tadepalli, Liang Zhao, Amarda Shehu. Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder. Bioinformatics Advances, to appear. [paper]

  13. [NeurIPS 2021] Yuanqi Du*, Shiyu Wang*, Xiaojie Guo, et al. GraphGT: Machine Learning Datasets for Graph Generation and Transformation. In Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) Dataset and Benchmark Track 2021.

  14. [KDD 2021] Xiaojie Guo, Yuanqi Du, Liang Zhao. Disentangled Deep Graph Generation for Spatial Networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), research track (acceptance rate: 15.4%), to appear, Singapore, Aug 2021. [paper] [code]

  15. [KAIS] Xiaojie Guo; Liang Zhao; Houman Homayoun; Sai Manoj Pudukotai Dinakarrao. Deep Graph Transformation for Attributed, Directed, and Signed Networks. Knowledge and Information Systems(KAIS), (impact factor: 2.936), to appear. [Bests of ICDM] [paper]

  16. [ICLR 2021] Xiaojie Guo, Yuanqi Du, Liang Zhao. Property Controllable Variational Autoencoder via Invertible Mutual Dependence. The 9th International Conference on Learning Representations (ICLR 2021), to appear. [paper]

  17. [KDD 2020] Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu and Yanfang Ye. Interpretable Deep Graph Generation with Node-edge Co-disentanglement. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), research track (acceptance rate: 16.8%), to appear, San Diego, USA, Aug 2020. [paper] [code]

  18. [IEEE Access] P D Sai Manoj, Xiaojie Guo, Hossein Sayadi , Cameron Nowzari, Avesta Sasan, Setareh Rafatirad, Liang Zhao, Houman Homayoun. Cognitive and Scalable Technique for Securing IoT Networks Against Malware Epidemics, in IEEE Access, vol. 8, pp. 138508-138528, 2020, doi: 10.1109/ACCESS.2020.3011919. [paper]

  19. [CSBW 2020] Yuanqi Du, Xiaojie Guo, Liang Zhao, and Amarda Shehu*. Interpretable Molecule Generation via Disentanglement Learning. ACM Conference of Bioinformatics and Computational Biology (BCB) Workshops: Computational Structural Biology Workshop (CSBW),2020.

  20. [ICDM 2019] Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, and Sai Dinakarrao. Deep Multi-attributed Graph Translation with Node-Edge Co-evolution.The 19th International Conference on Data Mining (ICDM 2019), long paper, (acceptance rate: 9.08%), Beijing, China. [Best Paper Award] [paper] [code]

  21. [CIKM 2019] Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng and Liang Zhao. Deep Classifier Cascades for Open World Recognition. The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), long paper, (acceptance rate: 19.4%), Beijing, China. [paper] [code]

  22. [AAAI 2018] Liang Zhao, Junxiang Wang, and Xiaojie Guo. Distant-supervision of heterogeneous multitask learning for social event forecasting with multilingual indicators. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), pp. 4498-4505, New Orleans, US, Feb 2018. [paper] [code]

  23. [SIGSPATIAL WS] Yuyang Gao., Xiaojie Guo, Liang Zhao. (2018, November). Local event forecasting and synthesis using unpaired deep graph translations. In Proceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News (pp. 1-8).

  24. [Advances in Mechanical Engineering] You Wei, Changqing Shen, Xiaojie Guo, et al. (2017). A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery. Advances in Mechanical Engineering, 9(6), 1687814017704146.

  25. [IISA 2017] Yuxuan Zhuang, Liang Chen, Xiaojie Guo. Air Quality Evaluation System Based on Stacked Auto-Encoder. In International Conference on Intelligent and Interactive Systems and Applications 2017 Jun 17 (pp. 63-68). Springer, Cham.

  26. [Measurement] Xiaojie Guo, Liang Chen, Changqing Shen. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement, 2016, v93, 490 502.

  27. [Applied Sciences] Xiaojie Guo, C hangqing Shen , L iang Chen. Deep fault recognizer: an integrated model to denoise and extract features for fault diagnosis in rotating machinery. Applied Science , 2016. Applied Sciences, 7(1), 41

  28. [IEEE CCC 2016] Liang Chen, Xiaojie Guo, et al. (2016, July). Human face recognition based on adaptive deep Convolution Neural Network. In 2016 35th Chinese Control Conference (CCC) (pp. 6967-6970). IEEE.

  29. [IEEE CCDC 2015] Xiaojie Guo , Liang Chen, et al. An improved K means algorithm and its application in the evaluation of air quality levels[C]// Chinese Control and Decision Conference. IEEE, 2015.