Tong Zhao | 赵通

Ph.D. student Dept. of Computer Science and Engineering University of Notre Dame Advisor: Meng Jiang 355 Fitzpatrick Hall Notre Dame, IN 46556 E-mail: tzhao2 \(\alpha\tau\) nd \(\delta o\tau\) edu Profiles: GitHub, LinkedIn, Google Scholar.

Research Interests

My research interests include graph machine learning, representation learning, anomaly detection, and user modeling.

CV

Please kindly find my Curriculum Vitae.

Recent News

• May 2022: I just graduated from University of Notre Dame and will be joining Snap Research as a Research Scientist this summer!

• Jan. 2022: I am invited to give a talk at ShenLanXueYuan on “Graph Data Augmentation for Graph Machine Learning”.

• Sept. 2021: I am honored to receive the Post Internship Fellowship from Amazon.

• Dec. 2020: I am honored to receive the 2020 Snap Research Fellowship from Snap Research.

Publications

Preprints

• Graph Data Augmentation for Graph Machine Learning: A Survey
  Tong Zhao, Gang Liu, Stephan Günnemann, and Meng Jiang.
  arXiv:2202.08871. [reading list]

Journal Papers

• Deep Multimodal Complementarity Learning
  Daheng Wang, Tong Zhao, Wenhao Yu, Nitesh Chawla, and Meng Jiang.
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022. [DOI]

• Neural-PDE: A RNN based Neural Network for Solving Time Dependent PDEs
  Yihao Hu, Tong Zhao, Shixin Xu, Zhiliang Xu, and Lizhe Lin.
  Communications in Information and Systems (CIS), 2022. [DOI]

• Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance
  Meng Jiang, Taeho Jung, Ryan Karl, and Tong Zhao.
  ACM Transactions on Intelligent Systems and Technology (TIST), 2021. [DOI]

• A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning
  Tong Zhao, Tianwen Jiang, Neil Shah, and Meng Jiang.
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. [DOI] [code & datasets]

• Modeling Co-evolution of Attributed and Structural Information in Graph Sequence
  Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh Chawla, and Meng Jiang.
  IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021. [DOI]

• Biomedical Knowledge Graphs Construction from Conditional Statements
  Tianwen Jiang, Qingkai Zeng, Tong Zhao, Bing Qin, Ting Liu, Nitesh Chawla, and Meng Jiang.
  IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 2020. [DOI]

Conference Papers

• Graph Rationalization with Environment-based Augmentations
  Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, and Meng Jiang.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022.

• Learning from Counterfactual Links for Link Prediction
  Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, and Meng Jiang.
  International Conference on Machine Learning (ICML), 2022. [code]

• Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts
  Wenhao Yu, Chenguang Zhu, Lianhui Qin, Zhihan Zhang, Tong Zhao, and Meng Jiang.
  Findings of Annual Meeting of the Association for Computational Linguistics (ACL), 2022. [code]

• Dynamic Attributed Graph Prediction with Conditional Normalizing Flows
  Daheng Wang, Tong Zhao, Nitesh Chawla, and Meng Jiang.
  IEEE International Conference on Data Mining (ICDM), 2021.

• Sentence-Permuted Paragraph Generation
  Wenhao Yu, Chenguang Zhu, Tong Zhao, Zhichun Guo, and Meng Jiang.
  Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021. [code]

• Action Sequence Augmentation for Early Graph-based Anomaly Detection
  Tong Zhao, Bo Ni, Wenhao Yu, Zhichun Guo, Neil Shah, and Meng Jiang.
  ACM International Conference on Information and Knowledge Management (CIKM), 2021. [slides] [code]

• A Unified View on Graph Neural Networks as Graph Signal Denoising
  Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, and Neil Shah.
  ACM International Conference on Information and Knowledge Management (CIKM), 2021.

• Data Augmentation for Graph Neural Networks
  Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, and Neil Shah.
  AAAI Conference on Artificial Intelligence (AAAI), 2021. [slides] [code]

• Error-bounded Graph Anomaly Loss for GNNs
  Tong Zhao, Chuchen Deng, Kaifeng Yu, Tianwen Jiang, Daheng Wang, and Meng Jiang.
  ACM International Conference on Information and Knowledge Management (CIKM), 2020. [slides] [code & datasets]

• Identifying Referential Intention with Heterogeneous Contexts
  Wenhao Yu, Mengxia Yu, Tong Zhao, and Meng Jiang.
  International World Wide Web Conference (WWW), 2020.

• CTGA: Graph-based Biomedical Literature Search
  Tianwen Jiang, Zhihan Zhang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, and Meng Jiang.
  IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019.

• Multi-input Multi-output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text
  Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, and Meng Jiang.
  Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.

• The Role of ‘‘Condition’’: A Novel Scientific Knowledge Graph Representation and Construction Model
  Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, and Meng Jiang.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019.

• Actionable Objective Optimization for Suspicious Behavior Detection on Large Bipartite Graphs
  Tong Zhao, Matthew Malir, and Meng Jiang.
  IEEE International Conference on Big Data (BigData), 2018. [slides]

Workshop and Demo Papers

• GNN-based Graph Anomaly Detection with Graph Anomaly Loss
  Tong Zhao, Chuchen Deng, Kaifeng Yu, Tianwen Jiang, Daheng Wang, and Meng Jiang.
  Workshop on Deep Learning on Graphs (DLG-KDD) at ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.

• Early Fraud Detection with Augmented Graph Learning
  Tong Zhao*, Bo Ni*, Wenhao Yu, and Meng Jiang.
  Workshop on Deep Learning on Graphs (DLG-KDD) at ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.

• Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
  Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, and Meng Jiang.
  Workshop on Deep Learning on Graphs (DLG-KDD) at ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020. (Best Paper Award)

• A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction
  Yang Zhou, Tong Zhao, and Meng Jiang.
  Workshop on Fact Extraction and Verification (FEVER) at Annual Meeting of the Association for Computational Linguistics (ACL), 2020.

• Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements
  Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, and Meng Jiang.
  International Workshop on Data Mining in Bioinformatics (BioKDD) at ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019.

• A Project Showcase for Planning Research Work towards Publishable Success
  Daheng Wang, Meng Jiang, Xueying Wang, Tong Zhao, Qingkai Zeng, and Nitesh Chawla.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018. (Demo)

Honors and Awards

• Amazon Post-internship Fellowship, Amazon. 2021.

• Snap Research Fellowship, Snap Inc. 2020.

• Best Paper Award, DLG-KDD. 2020.

• SIGIR Student Travel Grant, 29th ACM CIKM. 2020.

• Outstanding Teaching Assistant Honorable Mention, University of Notre Dame. 2019.