Publications

Tutorials

• Tong Zhao, Kaize Ding, Wei Jin, Gang Liu, Meng Jiang, Neil Shah
  Augmentation Methods for Graph Learning
  SIAM International Conferenceon Data Mining (SDM), 2023
• Wei Jin, Yao Ma, Yiqi Wang, Xiaorui Liu, Jiliang Tang, Yukuo Cen, Jiezhong Qiu, Jie Tang, Chuan Shi, Yanfang Ye, Jiawei Zhang, Philip S. Yu
  Graph Representation Learning: Foundations, Methods, Applications and Systems [slides]
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2021
• Yao Ma, Wei Jin, Yiqi Wang, Tyler Derr, Jiliang Tang
  Graph Neural Networks: Models and Applications [slides/video]
  AAAI Conference on Artificial Intelligence (AAAI), 2021
• Han Xu, Yaxin Li, Wei Jin, Jiliang Tang
  Adversarial Attacks and Defenses: Frontiers, Advances and Practice [website] [pdf]
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2020
• Yao Ma, Wei Jin, Jiliang Tang, Lingfei Wu, Tengfei Ma
  Graph Neural Networks: Models and Applications [website]
  AAAI Conference on Artificial Intelligence (AAAI), 2020

Preprints

• 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]
  arXiv 2202.08871, 2023
• Harry Shomer, Wei Jin, Juanhui Li, Yao Ma, Jiliang Tang
  Learning Representations for Hyper-Relational Knowledge Graphs [pdf]
  arXiv 2208.14322, 2022

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)

• Hua Liu*, Haoyu Han*, Wei Jin, Xiaorui Liu, Hui Liu
  Enhancing Graph Representations Learning with Decorrelated Propagation
  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
• 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
• 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