Publications

Preprints

• Zewen Liu, Guancheng Wan, B. Aditya Prakash, Max S. Y. Lau, Wei Jin
  A Review of Graph Neural Networks in Epidemic Modeling [pdf] [reading list]
  arXiv preprint 2024
• Hongliang Chi, Wei Jin, Charu Aggarwal, Yao Ma
  Precedence-Constrained Winter Value for Effective Graph Data Valuation [pdf]
  arXiv preprint 2024
• Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo, Jiliang Tang, Yi Chang
  Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective [pdf]
  arxiv preprint 2024

Book Chapter

• Yu Wang, Wei Jin, Tyler Derr
  Graph Neural Networks: Self-supervised Learning [Springer pdf] [preprint pdf]
  In Graph Neural Networks: Foundations, Frontiers, and Applications, Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao (Eds.). Springer. Chapter 18.

Book Translation

• Yiqi Wang, Wei Jin
  图深度学习, 电子工业出版社, 2021 [link]
  Chinese version of “Deep Learning on Graphs”, Cambridge University Press
  Best Seller in JD.com

Conference/Journal Papers (* indicates equal contributions)

• Yuchen Zhang, Tianle Zhang, Kai Wang, Ziyao Guo, Yuxuan Liang, Xavier Bresson, Wei Jin, Yang You
  Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching [pdf] [code]
  International Conference on Machine Learning (ICML), 2024

• Mohammad Hashemi*, Shengbo Gong*, Juntong Ni, Wenqi Fan, B. Aditya Prakash, Wei Jin
  A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation [reading list] [pdf]
  International Joint Conference on Artificial Intelligence (IJCAI), 2024

• Jiayuan Ding, Hongzhi Wen, Wenzhuo Tang, Renming Liu, Zhaoheng Li, Julian Venegas, Runze Su, Dylan Molho, Wei Jin, Yixin Wang, Qiaolin Lu, Lingxiao Li, Wangyang Zuo, Yi Chang, Yuying Xie, Jiliang Tang
  DANCE: A Deep Learning Library and Benchmark for Single-Cell Analysis [pdf]
  Genome Biology, 2024

• Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, Jiliang Tang
  Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs [pdf]
  SIGKDD Explorations, 2024

• Yi Nian, Yurui Chang, Wei Jin, Lu Lin
  Globally Interpretable Graph Learning via Distribution Matching [pdf]
  In Proceedings of the ACM Web Conference (WWW or TheWebConf), 2024

• Hongzhi Wen, Wenzhuo Tang, Xinnan Dai, Jiayuan Ding, Wei Jin, Yuying Xie, Jiliang Tang
  CellPLM: Pre-training of Cell Language Model Beyond Single Cells [pdf]
  International Conference on Learning Representations (ICLR), 2024

• Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han, Wei Jin, Haiyang Zhang, Hui Liu, Jiliang Tang
  Label-free Node Classification on Graphs with Large Language Models (LLMs) [pdf]
  International Conference on Learning Representations (ICLR), 2024

• Wei Jin*, Haitao Mao*, Zheng Li, Haoming Jiang, Chen Luo, Hongzhi Wen, Haoyu Han, Hanqing Lu, Zhengyang Wang, Ruirui Li, Zhen Li, Monica Xiao Cheng, Rahul Goutam, Haiyang Zhang, Karthik Subbian, Suhang Wang, Yizhou Sun, Jiliang Tang, Bing Yin, Xianfeng Tang
  Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation [pdf] [KDD Cup Website]
  Neural Information Processing Systems (NeurIPS), Track on Datasets and Benchmarks, 2023

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

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

• Wenzhuo Tang, Hongzhi Wen, Renming Liu, Jiayuan Ding, Wei Jin, Yuying Xie, Hui Liu, Jiliang Tang
  Single-Cell Multimodal Prediction via Transformers [pdf] [code]
  ACM on Conference on Information and Knowledge Management (CIKM), 2023

• Harry Shomer, Wei Jin, Juanhui Li, Yao Ma, Jiliang Tang
  Learning Representations for Hyper-Relational Knowledge Graphs [pdf]
  IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2023

• Hua Liu*, Haoyu Han*, Wei Jin, Xiaorui Liu, Hui Liu
  Enhancing Graph Representations Learning with Decorrelated Propagation [pdf]
  In Proceedings of 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2023

• Harry Shomer, Wei Jin, Wentao Wang, Jiliang Tang
  Toward Degree Bias in Embedding-Based Knowledge Graph Completion [pdf] [code]
  In Proceedings of the ACM Web Conference (WWW), 2023

• Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah
  Empowering Graph Representation Learning with Test-Time Graph Transformation [pdf] [code]
  In Proceedings of International Conference on LearningRepresentations (ICLR), 2023

• Wenqi Fan, Han Xu, Wei Jin, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, Jianping Wang, and Charu Aggarwal
  Jointly Attacking Graph Neural Network and its Explanations [pdf]
  In Proceedings of 39th IEEE International Conference on Data Engineering (ICDE 2023, Full paper)

• Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Bing Yin
  Condensing Graphs via One-Step Gradient Matching [pdf] [code]
  In Proceedings of 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2022

• Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang.
  Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective [pdf] [code]
  In Proceedings of 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2022

• Hongzhi Wen*, Jiayuan Ding*, Wei Jin*, Yiqi Wang*, Yuying Xie, Jiliang Tang [pdf] [code] [reading list]
  Graph Neural Networks for Multimodal Single-Cell Data Integration
  In Proceedings of 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2022
  The first place in the task of modality prediction at NeurIPS’21 Single-Cell Multimodal Data Integration

• Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li
  Graph Trend Networks for Recommendations [pdf]
  In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022

• Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah
  Graph Condensation for Graph Neural Networks [pdf] [code] [slides]
  In Proceedings of International Conference on LearningRepresentations (ICLR), 2022

• Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang
  Automated Self-Supervised Learning for Graphs [pdf] [code]
  In Proceedings of International Conference on Learning Representations (ICLR), 2022

• Lingxiao Zhao, Wei Jin, Leman Akoglu, Neil Shah [pdf] [code]
  From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
  In Proceedings of International Conference on Learning Representations (ICLR), 2022

• Yiqi Wang, Chaozhuo Li, Mingzheng Li, Wei Jin, Yuming Liu, Hao Sun, Xing Xie, Jiliang Tang
  Localized Graph Collaborative Filtering [pdf]
  In Proceedings of the SIAM International Conferenceon Data Mining (SDM), 2022

• Enyan Dai, Wei Jin, Hui Liu, Suhang Wang
  Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels
  In Proceedings of the 15th ACM Conference on Web Search and Data Mining (WSDM), 2022

• Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, Jiliang Tang
  Graph Neural Networks with Adaptive Residual [pdf]
  In Conference on Neural Information Processing Systems (NeurIPS), 2021

• Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu Aggarwal, Jiliang Tang
  Graph Feature Gating Networks [pdf]
  In Proceedings of the 2021 ACM on Conference on Information and Knowledge Management (CIKM), 2021

• Xiaorui Liu*, Wei Jin*, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang
  Elastic Graph Neural Networks [pdf] [code]
  In Proceedings of International Conference on Machine Learning (ICML 2021)
  Long Talk (top 3%)

• Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, Jiliang Tang
  Self-supervised Learning on Graphs: Deep Insights and New Direction [pdf] [code] [reading list]
  The Web Conference (WWW 2021) Workshop: Self-Supervised Learning for the Web

• Haochen Liu, Wei Jin, Hamid Karimi, Zitao Liu, Jiliang Tang
  The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification [pdf]
  In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL), Findings of ACL, 2021.

• Yaxin Li*, Wei Jin*, Han Xu, Jiliang Tang
  DeepRobust: A Platform for Adversarial Attacks and Defenses [pdf1] [pdf2] [library link]
  In Demonstrations Program of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021

• Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, Jiliang Tang
  Node Similarity Preserving Graph Convolutional Networks [pdf] [code]
  In Proceedings of the 14th ACM Conference on Web Search and Data Mining (WSDM), 2021

• Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, Jiliang Tang
  Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies [pdf] [code] [reading list]
  In ACM SIGKDD Explorations Newsletter (SIGKDD Explorations), 2020

• Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Jiliang Tang
  Graph Structure Learning for Robust Graph Neural Networks [pdf] [code] [slides]
  In Proceedings of 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2020
  Selected as Most Influential Papers in KDD by PaperDigest

• Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, Jian Yu
  Traffic Flow Prediction via Spatial Temporal Graph Neural Network [pdf]
  In Proceedings of the 29th International Conference on World Wide Web Companion (WWW), 2020
  Selected as Most Influential Papers in WWW by PaperDigest