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ORCIDWei Jin 0009
Person information
- affiliation: Emory University, Atlanta, GA, USA
- affiliation (Ph.D., 2023): Michigan State University (MSU), Department of Computer Science and Engineering, East Lansing, MI, USA
Other persons with the same name
- Wei Jin — disambiguation page
- Wei Jin 0001 — Google Inc., Mountain View, CA, USA (and 1 more)
- Wei Jin 0002
![[0000-0003-4552-5890]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0003-4552-5890")
— The Hong Kong Polytechnic University, Department of Electrical Engineering, Hong Kong - Wei Jin 0003 — Ningbo University, Faculty of Electrical Engineering and Computer Science, China
- Wei Jin 0004 — Shanghai Jiao Tong University, School of Microelectronics, China
- Wei Jin 0005 — Jiangxi University of Finance and Economics, School of Statistics, Nanchang, China
- Wei Jin 0006 — University of North Texas, USA (and 2 more)
- Wei Jin 0007 — University of West Georgia, Carrollton, USA
- Wei Jin 0008 — Dalian University of Technology, China
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2020 – today
- 2024
[j7]Jiayuan Ding, Lingxiao Li, Qiaolin Lu, Julian Venegas, Yixin Wang, Lidan Wu, Wei Jin, Hongzhi Wen, Renming Liu, Wenzhuo Tang, Xinnan Dai, Zhaoheng Li, Wangyang Zuo, Yi Chang, Yu Leo Lei, Lulu Shang, Patrick Danaher, Yuying Xie, Jiliang Tang:
SpatialCTD: A Large-Scale Tumor Microenvironment Spatial Transcriptomic Dataset to Evaluate Cell Type Deconvolution for Immuno-Oncology. J. Comput. Biol. 31(9): 871-885 (2024)- [j6]Dylan Molho, Jiayuan Ding, Wenzhuo Tang, Zhaoheng Li, Hongzhi Wen, Yixin Wang, Julian Venegas, Wei Jin, Renming Liu, Runze Su, Patrick Danaher, Robert Yang, Yu Leo Lei, Yuying Xie, Jiliang Tang:
Deep Learning in Single-cell Analysis. ACM Trans. Intell. Syst. Technol. 15(3): 40:1-40:62 (2024) - [c36]Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce C. Ho, Carl Yang:
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models. ACL (Findings) 2024: 15496-15523 - [c35]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). ICLR 2024 - [c34]Hongzhi Wen, Wenzhuo Tang, Xinnan Dai, Jiayuan Ding, Wei Jin, Yuying Xie, Jiliang Tang:
CellPLM: Pre-training of Cell Language Model Beyond Single Cells. ICLR 2024 - [c33]Mohammad Hashemi, Shengbo Gong, Juntong Ni, Wenqi Fan, B. Aditya Prakash, Wei Jin:
A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation. IJCAI 2024: 8058-8066 - [c32]Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo, Jiliang Tang, Yi Chang:
Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective. KDD 2024: 932-943 - [c31]Zewen Liu, Guancheng Wan, B. Aditya Prakash, Max S. Y. Lau, Wei Jin:
A Review of Graph Neural Networks in Epidemic Modeling. KDD 2024: 6577-6587 - [c30]Yi Nian, Yurui Chang, Wei Jin, Lu Lin:
Globally Interpretable Graph Learning via Distribution Matching. WWW 2024: 992-1002 - [c29]Wei Jin, Haohan Wang, Daochen Zha, Qiaoyu Tan, Yao Ma, Sharon Li, Su-In Lee:
DCAI: Data-centric Artificial Intelligence. WWW (Companion Volume) 2024: 1482-1485 - [i42]Hongliang Chi, Wei Jin, Charu Aggarwal, Yao Ma:
Precedence-Constrained Winter Value for Effective Graph Data Valuation. CoRR abs/2402.01943 (2024) - [i41]Mohammad Hashemi, Shengbo Gong, Juntong Ni, Wenqi Fan, B. Aditya Prakash, Wei Jin:
A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation. CoRR abs/2402.03358 (2024) - [i40]Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo, Jiliang Tang, Yi Chang:
Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective. CoRR abs/2402.08228 (2024) - [i39]Juanhui Li, Haoyu Han, Zhikai Chen, Harry Shomer, Wei Jin, Amin Javari, Jiliang Tang:
Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation. CoRR abs/2402.08921 (2024) - [i38]Zewen Liu, Guancheng Wan, B. Aditya Prakash, Max S. Y. Lau, Wei Jin:
A Review of Graph Neural Networks in Epidemic Modeling. CoRR abs/2403.19852 (2024) - [i37]Zewen Liu, Yunxiao Li, Mingyang Wei, Guancheng Wan, Max S. Y. Lau, Wei Jin:
EpiLearn: A Python Library for Machine Learning in Epidemic Modeling. CoRR abs/2406.06016 (2024) - [i36]Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin, Bahare Fatemi, Anton Tsitsulin, Bryan Perozzi, Hui Liu, Jiliang Tang:
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights. CoRR abs/2406.10727 (2024) - [i35]Yu Song, Haitao Mao, Jiachen Xiao, Jingzhe Liu, Zhikai Chen, Wei Jin, Carl Yang, Jiliang Tang, Hui Liu:
A Pure Transformer Pretraining Framework on Text-attributed Graphs. CoRR abs/2406.13873 (2024) - [i34]Shengbo Gong, Juntong Ni, Noveen Sachdeva, Carl Yang, Wei Jin:
GC-Bench: A Benchmark Framework for Graph Condensation with New Insights. CoRR abs/2406.16715 (2024) - [i33]Kai Guo, Zewen Liu, Zhikai Chen, Hongzhi Wen, Wei Jin, Jiliang Tang, Yi Chang:
Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness. CoRR abs/2407.12068 (2024) - [i32]Hang Li, Wei Jin, Geri Skenderi, Harry Shomer, Wenzhuo Tang, Wenqi Fan, Jiliang Tang:
Sub-graph Based Diffusion Model for Link Prediction. CoRR abs/2409.08487 (2024) - 2023
- [j5]Yiqi Wang, Yao Ma, Wei Jin, Chaozhuo Li, Charu Aggarwal, Jiliang Tang:
Customized Graph Nerual Networks. IEEE Data Eng. Bull. 46(2): 108-125 (2023) - [j4]Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Günnemann, Neil Shah, Meng Jiang:
Graph Data Augmentation for Graph Machine Learning: A Survey. IEEE Data Eng. Bull. 46(2): 140-165 (2023) - [j3]Xin Juan, Fengfeng Zhou, Wentao Wang, Wei Jin, Jiliang Tang, Xin Wang:
INS-GNN: Improving graph imbalance learning with self-supervision. Inf. Sci. 637: 118935 (2023) - [j2]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. SIGKDD Explor. 25(2): 42-61 (2023) - [c28]Harry Shomer, Wei Jin, Juanhui Li, Yao Ma, Hui Liu:
Learning Representations for Hyper-Relational Knowledge Graphs. ASONAM 2023: 253-257 - [c27]Wenzhuo Tang, Hongzhi Wen, Renming Liu, Jiayuan Ding, Wei Jin, Yuying Xie, Hui Liu, Jiliang Tang:
Single-Cell Multimodal Prediction via Transformers. CIKM 2023: 2422-2431 - [c26]Wenqi Fan, Han Xu, Wei Jin, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, Jianping Wang, Charu C. Aggarwal:
Jointly Attacking Graph Neural Network and its Explanations. ICDE 2023: 654-667 - [c25]Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah:
Empowering Graph Representation Learning with Test-Time Graph Transformation. ICLR 2023 - [c24]Hua Liu, Haoyu Han, Wei Jin, Xiaorui Liu, Hui Liu:
Enhancing Graph Representations Learning with Decorrelated Propagation. KDD 2023: 1466-1476 - [c23]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. NeurIPS 2023 - [c22]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? NeurIPS 2023 - [c21]Harry Shomer, Wei Jin, Wentao Wang, Jiliang Tang:
Toward Degree Bias in Embedding-Based Knowledge Graph Completion. WWW 2023: 705-715 - [i31]Hongzhi Wen, Wenzhuo Tang, Wei Jin, Jiayuan Ding, Renming Liu, Feng Shi, Yuying Xie, Jiliang Tang:
Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation. CoRR abs/2302.03038 (2023) - [i30]Harry Shomer, Wei Jin, Wentao Wang, Jiliang Tang:
Toward Degree Bias in Embedding-Based Knowledge Graph Completion. CoRR abs/2302.05044 (2023) - [i29]Wenzhuo Tang, Hongzhi Wen, Renming Liu, Jiayuan Ding, Wei Jin, Yuying Xie, Hui Liu, Jiliang Tang:
Single-Cell Multimodal Prediction via Transformers. CoRR abs/2303.00233 (2023) - [i28]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? CoRR abs/2306.01323 (2023) - [i27]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. CoRR abs/2307.03393 (2023) - [i26]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. CoRR abs/2307.09688 (2023) - [i25]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). CoRR abs/2310.04668 (2023) - [i24]Kaiqi Yang, Haoyu Han, Wei Jin, Hui Liu:
Augment with Care: Enhancing Graph Contrastive Learning with Selective Spectrum Perturbation. CoRR abs/2310.13845 (2023) - [i23]Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, Wei Jin, Joyce C. Ho, Carl Yang:
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models. CoRR abs/2311.00287 (2023) - 2022
- [c20]Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang:
Automated Self-Supervised Learning for Graphs. ICLR 2022 - [c19]Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah:
Graph Condensation for Graph Neural Networks. ICLR 2022 - [c18]Lingxiao Zhao, Wei Jin, Leman Akoglu, Neil Shah:
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness. ICLR 2022 - [c17]Wei Jin, Xiaorui Liu, Yao Ma, Charu C. Aggarwal, Jiliang Tang:
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective. KDD 2022: 709-719 - [c16]Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Bing Yin:
Condensing Graphs via One-Step Gradient Matching. KDD 2022: 720-730 - [c15]Hongzhi Wen, Jiayuan Ding, Wei Jin, Yiqi Wang, Yuying Xie, Jiliang Tang:
Graph Neural Networks for Multimodal Single-Cell Data Integration. KDD 2022: 4153-4163 - [c14]Yiqi Wang, Chaozhuo Li, Mingzheng Li, Wei Jin, Yuming Liu, Hao Sun, Xing Xie, Jiliang Tang:
Localized Graph Collaborative Filtering. SDM 2022: 540-548 - [c13]Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li:
Graph Trend Filtering Networks for Recommendation. SIGIR 2022: 112-121 - [c12]Enyan Dai, Wei Jin, Hui Liu, Suhang Wang:
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels. WSDM 2022: 181-191 - [i22]Enyan Dai, Wei Jin, Hui Liu, Suhang Wang:
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels. CoRR abs/2201.00232 (2022) - [i21]Hongzhi Wen, Jiayuan Ding, Wei Jin, Yuying Xie, Jiliang Tang:
Graph Neural Networks for Multimodal Single-Cell Data Integration. CoRR abs/2203.01884 (2022) - [i20]Wei Jin, Xiaorui Liu, Yao Ma, Charu C. Aggarwal, Jiliang Tang:
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective. CoRR abs/2206.07743 (2022) - [i19]Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Bin Ying:
Condensing Graphs via One-Step Gradient Matching. CoRR abs/2206.07746 (2022) - [i18]Harry Shomer, Wei Jin, Juan-Hui Li, Yao Ma, Jiliang Tang:
Learning Representations for Hyper-Relational Knowledge Graphs. CoRR abs/2208.14322 (2022) - [i17]Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah:
Empowering Graph Representation Learning with Test-Time Graph Transformation. CoRR abs/2210.03561 (2022) - [i16]Yiqi Wang, Chaozhuo Li, Wei Jin, Rui Li, Jianan Zhao, Jiliang Tang, Xing Xie:
Test-Time Training for Graph Neural Networks. CoRR abs/2210.08813 (2022) - [i15]Dylan Molho, Jiayuan Ding, Zhaoheng Li, Hongzhi Wen, Wenzhuo Tang, Yixin Wang, Julian Venegas, Wei Jin, Renming Liu, Runze Su, Patrick Danaher, Robert Yang, Yu Leo Lei, Yuying Xie, Jiliang Tang:
Deep Learning in Single-Cell Analysis. CoRR abs/2210.12385 (2022) - 2021
- [c11]Yaxin Li, Wei Jin, Han Xu, Jiliang Tang:
DeepRobust: a Platform for Adversarial Attacks and Defenses. AAAI 2021: 16078-16080 - [c10]Haochen Liu, Wei Jin, Hamid Karimi, Zitao Liu, Jiliang Tang:
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification. ACL/IJCNLP (Findings) 2021: 74-85 - [c9]Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu C. Aggarwal, Jiliang Tang:
Graph Feature Gating Networks. CIKM 2021: 813-822 - [c8]Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang:
Elastic Graph Neural Networks. ICML 2021: 6837-6849 - [c7]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. KDD 2021: 4044-4045 - [c6]Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, Jiliang Tang:
Graph Neural Networks with Adaptive Residual. NeurIPS 2021: 9720-9733 - [c5]Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, Jiliang Tang:
Node Similarity Preserving Graph Convolutional Networks. WSDM 2021: 148-156 - [c4]Wei Jin:
Graph Mining with Graph Neural Networks. WSDM 2021: 1119-1120 - [i14]Haochen Liu, Wei Jin, Hamid Karimi, Zitao Liu, Jiliang Tang:
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification. CoRR abs/2105.02778 (2021) - [i13]Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu C. Aggarwal, Jiliang Tang:
Graph Feature Gating Networks. CoRR abs/2105.04493 (2021) - [i12]Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang:
Automated Self-Supervised Learning for Graphs. CoRR abs/2106.05470 (2021) - [i11]Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang:
Elastic Graph Neural Networks. CoRR abs/2107.06996 (2021) - [i10]Wenqi Fan, Wei Jin, Xiaorui Liu, Han Xu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, Jianping Wang, Charu C. Aggarwal:
Jointly Attacking Graph Neural Network and its Explanations. CoRR abs/2108.03388 (2021) - [i9]Yiqi Wang, Chaozhuo Li, Mingzheng Li, Wei Jin, Yuming Liu, Hao Sun, Xing Xie:
Localized Graph Collaborative Filtering. CoRR abs/2108.04475 (2021) - [i8]Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li:
Graph Trend Networks for Recommendations. CoRR abs/2108.05552 (2021) - [i7]Lingxiao Zhao, Wei Jin, Leman Akoglu, Neil Shah:
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness. CoRR abs/2110.03753 (2021) - [i6]Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah:
Graph Condensation for Graph Neural Networks. CoRR abs/2110.07580 (2021) - 2020
- [j1]Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, Jiliang Tang:
Adversarial Attacks and Defenses on Graphs. SIGKDD Explor. 22(2): 19-34 (2020) - [c3]Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Jiliang Tang:
Graph Structure Learning for Robust Graph Neural Networks. KDD 2020: 66-74 - [c2]Han Xu, Yaxin Li, Wei Jin, Jiliang Tang:
Adversarial Attacks and Defenses: Frontiers, Advances and Practice. KDD 2020: 3541-3542 - [c1]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. WWW 2020: 1082-1092 - [i5]Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, Jiliang Tang:
Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study. CoRR abs/2003.00653 (2020) - [i4]Yaxin Li, Wei Jin, Han Xu, Jiliang Tang:
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses. CoRR abs/2005.06149 (2020) - [i3]Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Jiliang Tang:
Graph Structure Learning for Robust Graph Neural Networks. CoRR abs/2005.10203 (2020) - [i2]Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, Jiliang Tang:
Self-supervised Learning on Graphs: Deep Insights and New Direction. CoRR abs/2006.10141 (2020) - [i1]Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, Jiliang Tang:
Node Similarity Preserving Graph Convolutional Networks. CoRR abs/2011.09643 (2020)
Coauthor Index
[j7] [j6] [c35] [c34] [c32] [i40] [i39] [i36] [i35] [i33] [i32] [j5] [j3] [j2] [c27] [c26] [c25] [c23] [c22] [c21] [i31] [i30] [i29] [i28] [i27] [i26] [i25] [c20] [c19] [c17] [c16] [c15] [c14] [c13] [i21] [i20] [i19] [i18] [i17] [i16] [i15] [c11] [c10] [c9] [c8] [c7] [c6] [c5] [i14] [i13] [i12] [i11] [i10] [i8] [i6] [j1] [c3] [c2] [c1] [i5] [i4] [i3] [i2] [i1]
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