Tong Zhao | 赵通

Research Scientist at Snap Research 2025 1st Avenue Seattle, WA 98121 Personal E-mail: tong \(\alpha\tau\) tzhao \(\delta o\tau\) io Work E-mail: tong \(\alpha\tau\) snap \(\delta o\tau\) com ND Alumni E-mail: tzhao2 \(\alpha\tau\) alumni \(\delta o\tau\) nd \(\delta o\tau\) edu School E-mail (deprecated): tzhao2 \(\alpha\tau\) nd \(\delta o\tau\) edu Profiles: GitHub, LinkedIn, Google Scholar.

To prospective interns: Please apply on the Snap Research website here, and e-mail me afterwards.

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 2023: Our tutorial Large-Scale Graph Neural Networks: the Past and New Frontiers was accepted to KDD 2023. Stay tuned and see you in Long Beach!

• Nov. 2022: I am thrilled to present our tutorial on Augmentation Methods for Graph Learning at SDM 2023. Stay tuned and see you in Minneapolis!

• Oct. 2022: Looking forward to giving a keynote at MLoG Workshop. See you in Orlando!

• July 2022: Looking forward to giving a keynote at Mis2-TrueFact Workshop. See you in Washington DC!

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

Publications

Conference Papers

• GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation
  Mingxuan Ju, Tong Zhao, Wenhao Yu, Neil Shah, and Yanfang Ye.
  Conference on Neural Information Processing Systems (NeruIPS), 2023.

• Data-Centric Learning from Unlabeled Graphs with Diffusion Model
  Gang Liu, Eric Inae, Tong Zhao, Jiaxin Xu, Tengfei Luo, and Meng Jiang.
  Conference on Neural Information Processing Systems (NeruIPS), 2023.

• Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
  Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, and Jiliang Tang.
  Conference on Neural Information Processing Systems (NeruIPS), 2023.

• Semi-supervised Graph Imbalanced Regression
  Gang Liu, Tong Zhao, Eric Inae, Tengfei Luo, and Meng Jiang.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023.

• CARL-G: Clustering-Accelerated Representation Learning on Graphs
  William Shiao, Uday Singh Saini, Yozen Liu, Tong Zhao, Neil Shah, and Evangelos E Papalexakis.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023.

• Linkless Link Prediction via Relational Distillation
  Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh Chawla, Neil Shah, and Tong Zhao.
  International Conference on Machine Learning (ICML), 2023. [code]

• Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
  Mingxuan Ju, Tong Zhao, Qianlong Wen, Wenhao Yu, Neil Shah, Yanfang Ye, and Chuxu Zhang.
  International Conference on Learning Representations (ICLR), 2023.

• MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
  Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, and Neil Shah.
  International Conference on Learning Representations (ICLR), 2023.

• Empowering Graph Representation Learning with Test-Time Graph Transformation
  Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, and Neil Shah.
  International Conference on Learning Representations (ICLR), 2023.

• Link Prediction with Non-Contrastive Learning
  William Shiao, Zhichun Guo, Tong Zhao, Evangelos E Papalexakis, Yozen Liu, and Neil Shah.
  International Conference on Learning Representations (ICLR), 2023.

• AutoGDA: Automated Graph Data Augmentation for Node Classification
  Tong Zhao, Xianfeng Tang, Danqing Zhang, Haoming Jiang, Nikhil Rao, Yiwei Song, Pallav Agrawal, Karthik Subbian, Bing Yin, and Meng Jiang.
  Learning on Graphs Conference (LoG), 2022.

• Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering
  Mingxuan Ju, Wenhao Yu, Tong Zhao, Chuxu Zhang, and Yanfang Ye.
  Findings of Empirical Methods in Natural Language Processing (EMNLP), 2022. [code]

• 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. [code]

• 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]

Journal Papers

• Graph Data Augmentation for Graph Machine Learning: A Survey
  Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Günnemann, Neil Shah, and Meng Jiang.
  IEEE Data Engineering Bulletin (DEBULL), 2023. [reading list]

• 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]

Workshop and Demo Papers

• Enhancing Multi-hop Connectivity for Graph Convolutional Networks
  Songtao Liu, Shixiong Jing, Tong Zhao, Zengfeng Huang, and Dinghao Wu.
  Workshop on Pre-training at International Conference on Machine Learning (ICML), 2022.

• 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.