Publications (Back to Homepage)

2021

• Deep Fraud Detection on Non-attributed Graph.
  Chen Wang, Yingtong Dou, Min Chen, Jia Chen, Zhiwei Liu, Philip S. Yu.
  IEEE BigData. 2021.
  [Paper][BibTeX]

• Cross-lingual COVID-19 Fake News Detection.
  Jiangshu Du, Yingtong Dou, Congying Xia, Limeng Cui, Jing Ma, Philip S. Yu.
  IEEE ICDMW. 2021.
  [Paper][Data][BibTeX]

• Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks.
  Hao Peng, Ruitong Zhang, Yingtong Dou, Renyu Yang, Jingyi Zhang, Philip S. Yu.
  ACM TOIS. 2021.
  [Paper][Code][BibTeX]

• User Preference-aware Fake News Detection.
  Yingtong Dou, Kai Shu, Congying Xia, Philip S. Yu, Lichao Sun.
  ACM SIGIR. 2021.
  [Paper][Code][Slides][PyG Example][DGL Example][Data][Chinese Blog][BibTeX]

• ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation.
  Liangwei Yang, Zhiwei Liu, Yingtong Dou, Jing Ma, Philip S. Yu.
  ACM SIGIR. 2021.
  [Paper][Code][BibTeX]

• Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks.
  Jianxin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He.
  IEEE TKDE. 2021.
  [Paper][Code][BibTeX]

• Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs.
  Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, Jianxin Li, Philip S. Yu.
  The Web Conference. 2021.
  [Paper][Code][BibTeX]

2020

• Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters.
  Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, Philip S. Yu.
  [Paper][Code][DGL Example][Slides][BibTeX]

• Robust Spammer Detection by Nash Reinforcement Learning.
  Yingtong Dou, Guixiang Ma, Philip S. Yu, Sihong Xie.
  ACM SIGKDD. 2020.
  [Paper][Code][Slides][Video][Chinese Blog][BibTeX]

• Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection.
  Zhiwei Liu, Yingtong Dou, Philip S. Yu, Yutong Deng, Hao Peng.
  ACM SIGIR. 2020.
  [Paper][Code][Slides][Chinese Blog][BibTeX]

Before 2020

• Uncovering Download Fraud Activities in Mobile App Markets.
  Yingtong Dou, Weijian Li, Zhirong Liu, Zhenhua Dong, Jiebo Luo, Philip S. Yu.
  ACM/IEEE ASONAM. 2019.
  [Paper][Slides][English Blog][BibTeX]

• A Review of Recent Advance in Online Spam Detection.
  Yingtong Dou.
  Technical Report. Mar. 2019.
  [Paper][Slides][BibTeX]

• 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][Slides][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][Extended Journal Version][BibTeX]

Projects

• Misinformation Detection

Introduction We investigate various problems and challenges regarding fact-checking and fake news classification tasks. Some of the problems are: user endogeneous preference encoding, zero/few-shot fake news detection, and fake news detection under adversarial settings.

Resources Project Homepage. A collection of GNN-based fake news detectors and two fake news propagation graph datasets. Benchmarking GNN-based fake news detection.

• Robust Fraud Detection

Introduction The adversarial behaviors between advanced spammers and the defenders make the fraud detection problem more challenging. We employ various machine learning methods to simulate the adversarial behavior between two sides and devise robust fraud detectors.

Resources Project Homepage. A curated list of up-to-date papers on fraud detection. Deep and Non-deep Graph-based Toolboxes for Fraud Detection. Introduction Slides

• Securing Graph-based Learning Models

Introduction Despite the advance of graph-based learning models like probabilistic graphical models and graph neural networks, their applications to various areas also face diverse threats. We investigate the practical threats of SOTA models and secure them from multiple channels.

Resources A survey from our lab on adversarial attack and defense on graph data. A list of papers on graph adversarial learning.

• Suspicious Behavior Modeling in Mobile App Markets

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, and aim to design classifers with multi-view and multi-source information according to the intention of the fraudsters.

Resources My blog post about download fraud in App markets.