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, computational behavior modeling, anomaly detection, and graph mining.

CV

Please kindly find my Curriculum Vitae.

Recent News

• Jan. 2021: I will join Amazon as an Applied Scientist Intern for Summer 2021!

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

Publications

Preprints

• Counterfactual Graph Learning for Link Prediction
  Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, and Meng Jiang.
  arXiv:2106.02172.

• Early Anomaly Detection by Learning and Forecasting Behavior
  Tong Zhao, Bo Ni, Wenhao Yu, and Meng Jiang.
  arXiv:2010.10016.

• Federated Dynamic GNN with Secure Aggregation
  Meng Jiang, Taeho Jung, Ryan Karl, and Tong Zhao.
  arXiv:2009.07351.

• Neural Time-Dependent Partial Differential Equation
  Yihao Hu, Tong Zhao, Zhiliang Xu, and Lizhen Lin.
  arXiv:2009.03892.

• Sentence-Permuted Paragraph Generation
  Wenhao Yu, Chenguang Zhu, Tong Zhao, Zhichun Guo, and Meng Jiang.
  arXiv:2104.07228. [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.
  arXiv:2010.01777.

Conference Papers

• 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.
  Proceeding of 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.
  Proceedings of the 2019 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. (Oral) [slides]

Journal Papers

• 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.

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

• 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.