Tong Zhao | 赵通
 |
Ph.D. student |
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
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
Counterfactual Graph Learning for Link Prediction
Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, and Meng Jiang.
arXiv:2106.02172.Neural Time-Dependent Partial Differential Equation
Yihao Hu, Tong Zhao, Zhiliang Xu, and Lizhen Lin.
arXiv:2009.03892.
Journal Papers
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.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. [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.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.
Conference Papers
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. (Oral) [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.