I am currently a Computer Science Ph.D. student in Big Data and Social Computing (BDSC) Lab of the University of Illinois at Chicago. My supervisor is Prof. Philip S. Yu. Before joining UIC, I got my Bachelor’s Degree from the Beijing University of Posts and Telecommunications and the Queen Mary University of London. My research interests are graph mining, spam detection and social network analysis. I am interested in designing algorithms dealing with challenging problems in social networks and applying social computing techniques into various areas.

View my CV (Updated at January 2019)


  • A Novel Centrality Cascading Based Edge Parameter Evaluation Method for Robust Influence Maximization.
    Xiaolong Deng, Yingtong Dou, Tiejun Lv, Nguyen QVH.
    IEEE Access. 2017. [Paper][Code][BibTeX]
  • CPS Model Based Online Opinion Governance Modeling and Evaluation of Emergency Accidents.
    Xiaolong Deng, Yingtong Dou, Yihua Huang.
    EMGIS in ACM SIGSPATIAL. 2016. [Paper][Slides][Journal Version][BibTeX]

Technical Reports

  • A Review of Recent Advance in Online Spam Detection.
    Yingtong Dou.
    March 2019. [Paper][Slides]

Working Projects

  • Suspicious Behavior Modeling in Mobile App Markets

projects1 Introduction Mobile App Markets like App Store and Google Play involves many fraudsters like spammers, botnets and crowd workers. We investigate the underground market of trading app downloads and reviews, design classifers with multi-view and multi-source information according to the intention of the fraudsters. We also want to develop scalable and robust graph based classfication models.
Resources My intro slides about graph-based spam detection, Meng Jiang’s survey on suspicious behavior modeling, Srijan Kumar’s survey on online false information study.

  • Securing Graph-based Classfication Model

projects2 Introduction The long lasting campaign between the fraudsters and online review platforms like Yelp and TripAdvisor makes the security of classifiers become very important. We aim to improve the robustness of graphical classfiers like message passing algorithm and graph convolutional neural network against various kinds of adversarial examples.
Resources A survey from our lab on adversarial attack and defense on graph data, KDD18 best paper on adversarial attack on neural networks for graph data, ICML18 paper on adversarial attack on graph structured data.


  • [1/2019] Move my personal website from Wordpress to Github.
  • [1/2019] Give a talk about graph based spammer detection at Tencent.
  • [5/2018] Start summer intern at the Search and Recommendation Group of Noah’s Ark Lab.
  • [2/2018] Publish a dataset with Chinese O2O service Wechat lucky package sharing log between two years.
  • [More]