Senior Research Scientist at Snap Research |
To prospective interns: Please apply on the Snap Research website here, and e-mail me afterwards.
My research interests include machine learning and data mining with their applications on social recommendation, user modeling, etc.
Please kindly find my Curriculum Vitae.
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 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”.
Graph Transformers for Large Graphs
Vijay Prakash Dwivedi, Yozen Liu, Anh Tuan Luu, Xavier Bresson, Neil Shah, and Tong Zhao.
arXiv: 2312.11109, 2023.
Neural Scaling Laws on Graphs
Jingzhe Liu, Haitao Mao, Zhikai Chen, Tong Zhao, Neil Shah, and Jiliang Tang.
arXiv: 2402.02054, 2024.
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.
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.
arXiv: 2403.18280, 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 (NeruIPS), 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 (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.
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]
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.