Tong Zhao | 赵通

Senior Research Scientist at Snap Research Contacts: 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 machine learning and data mining with their applications on social recommendation, user modeling, etc.

CV

Please kindly find my Curriculum Vitae.

RecentOutdated News

• May 2025: Our hands-on tutorial Training Industry-scale GNNs with GiGL was accepted by KDD 2025. Stay tuned and see you in Toronto!

• Call for Paper: We are organzing a new research topic Graph Machine Learning at Large Scale at Frontiers in Big Data. Please consider submitting your work!

• May 2023: Our tutorial Large-Scale Graph Neural Networks: the Past and New Frontiers was accepted by 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

Preprints

• Graph Transformers for Large Graphs
  Vijay Prakash Dwivedi, Yozen Liu, Anh Tuan Luu, Xavier Bresson, Neil Shah, and Tong Zhao.
  arXiv: 2312.11109, 2023.

• Node Duplication Improves Cold-start Link Prediction
  Zhichun Guo, Tong Zhao, Yozen Liu, Kaiwen Dong, William Shiao, Neil Shah, and Nitesh Chawla.
  arXiv: 2402.09711, 2024.

• HARec: Hyperbolic Graph-LLM Alignment for Exploration and Exploitation in Recommender Systems
  Qiyao Ma, Menglin Yang, Mingxuan Ju, Tong Zhao, Neil Shah, and Rex Ying.
  arXiv: 2411.13865, 2024.

• Enhancing Item Tokenization for Generative Recommendation through Self-Improvement
  Runjin Chen, Mingxuan Ju, Ngoc Bui, Dimosthenis Antypas, Stanley Cai, Xiaopeng Wu, Leonardo Neves, Zhangyang Wang, Neil Shah, and Tong Zhao.
  arXiv: 2412.17171, 2024.

• Retrieval-Augmented Generation with Graphs (GraphRAG)
  Haoyu Han, Yu Wang, …, Tong Zhao, Neil Shah, Amin Javari, Yinglong Xia, and Jiliang Tang.
  arXiv: 2501.00309, 2024.

• Beyond Unimodal Boundaries: Generative Recommendation with Multimodal Semantics
  Jing Zhu, Mingxuan Ju, Yozen Liu, Danai Koutra, Neil Shah, and Tong Zhao.
  arXiv: 22503.23333, 2025.

Peer-reviewed Papers

• GiGL: Large-Scale Graph Neural Networks at Snapchat
  Tong Zhao, Yozen Liu, Matthew Kolodner, Kyle Montemayor, …, Shubham Vij, and Neil Shah.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2025.

• Revisiting Self-Attention for Cross-Domain Sequential Recommendation
  Mingxuan Ju, Leonardo Neves, Bhuvesh Kumar, Liam Collins, Tong Zhao, Yuwei Qiu, Qing Dou, Sohail Nizam, Sen Yang, and Neil Shah.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2025.

• On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective
  Donald Loveland, Mingxuan Ju, Tong Zhao, Neil Shah, and Danai Koutra.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2025.

• Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning
  Ngoc Bui, Menglin Yang, Runjin Chen, Leonardo Neves, Mingxuan Ju, Rex Ying, Neil Shah, and Tong Zhao.
  International Conference on Machine Learning (ICML), 2025.

• Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
  Rui Xue, Tong Zhao, Neil Shah, and Xiaorui Liu.
  International Conference on Machine Learning (ICML), 2025.

• Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat
  Mingxuan Ju, Leonardo Neves, Bhuvesh Kumar, Liam Collins, Tong Zhao, Yuwei Qiu, Ching Dou, Yang Zhou, Sohail Nizam, Rengim Aykan Ozturk, Yvette Liu, Sen Yang, Manish Malik, and Neil Shah.
  SIGIR Symposium on IR in Practice (SIRIP), 2025.

• Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning
  Jing Zhu, Yuhang Zhou, Shengyi Qian, Zhongmou He, Tong Zhao, Neil Shah, and Danai Koutra.
  Conference on Computer Vision and Pattern Recognition (CVPR), 2025.

• GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems
  Xinyi Wu, Donald Loveland, Runjin Chen, Yozen Liu, Xin Chen, Leonardo Neves, Ali Jadbabaie, Mingxuan Ju, Neil Shah, and Tong Zhao.
  International World Wide Web Conference (WWW), 2025.

• Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank
  Donald Loveland, Xinyi Wu, Tong Zhao, Danai Koutra, Neil Shah, and Mingxuan Ju.
  International World Wide Web Conference (WWW), 2025.

• Improving Out-of-Vocabulary Handling in Recommendation Systems
  William Shiao, Mingxuan Ju, Zhichun Guo, Xin Chen, Evangelos Papalexakis, Tong Zhao, Neil Shah, and Yozen Liu.
  REL workshop at International World Wide Web Conference (WWW), 2025.

• Towards Neural Scaling Laws on Graphs
  Jingzhe Liu, Haitao Mao, Zhikai Chen, Tong Zhao, Neil Shah, and Jiliang Tang.
  Learning on Graphs Conference (LoG), 2024.

• How Does Message Passing Improve Collaborative Filtering?
  Mingxuan Ju, William Shiao, Zhichun Guo, Yanfang Ye, Yozen Liu, Neil Shah, and Tong Zhao.
  Conference on Neural Information Processing Systems (NeurIPS), 2024.

• Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems
  Matthew Kolodner, Mingxuan Ju, Zihao Fan, Tong Zhao, Elham Ghazizadeh, Yan Wu, Neil Shah, and Yozen Liu.
  RobustRecSys workshop at ACM Conference on Recommender Systems (RecSys), 2024.

• Improving Embedding-Based Retrieval in Friend Recommendation with ANN Query Expansion
  Pau Kung, Zihao Fan, Tong Zhao, Yozen Liu, Lucas Lai, Jiahui Shi, Yan Wu, Jun Yu, Neil Shah, and Ganesh Venkataraman.
  SIGIR Symposium on IR in Practice (SIRIP), 2024.

• LLaGA: Large Language and Graph Assistant
  Runjin Chen, Tong Zhao, Ajay Jaiswal, Neil Shah, and Zhangyang Wang.
  International Conference on Machine Learning (ICML), 2024.

• Graph Foundational Models
  Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Michael Galkin, and Jiliang Tang.
  International Conference on Machine Learning (ICML), 2024.

• Learning from Graphs Beyond Message Passing Nerual Networks
  Tong Zhao, Neil Shah, and Elham Ghazizadeh.
  International Conference on Learning Representations - Tiny Paper (ICLR), 2024.

• Revisiting Link Prediction: a Data Perspective
  Haitao Mao, Juanhui Li, Harry Shomer, Bingheng Li, Wenqi Fan, Yao Ma, Tong Zhao, Neil Shah, and Jiliang Tang.
  International Conference on Learning Representations (ICLR), 2024.

• A Topological Perspective on Demystifying GNN-based Link Prediction Performance
  Yu Wang, Tong Zhao, Yuying Zhao, Yunchao Liu, Xueqi Cheng, Neil Shah, and Tyler Derr.
  International Conference on Learning Representations (ICLR), 2024.

• 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 (NeurIPS), 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 (NeurIPS), 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 (NeurIPS), 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.

• 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]

• 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]

• 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]

• 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]

• 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]

• 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.

• 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]

• CTGA: Graph-based Biomedical Literature Search
  Tianwen Jiang, Zhihan Zhang, Tong Zhao, Bing Qin, Ting Liu, Nitesh 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 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 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]

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.