Wei Jin
Cited by
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Graph Structure Learning for Robust Graph Neural Networks
W Jin, Y Ma, X Liu, X Tang, S Wang, J Tang
KDD 2020, 2020
Traffic flow prediction via spatial temporal graph neural network
X Wang, Y Ma, Y Wang, W Jin, X Wang, J Tang, C Jia, J Yu
Proceedings of the web conference 2020, 1082-1092, 2020
Adversarial attacks and defenses on graphs
W Jin, Y Li, H Xu, Y Wang, S Ji, C Aggarwal, J Tang
ACM SIGKDD Explorations Newsletter 22 (2), 19-34, 2021
Node Similarity Preserving Graph Convolutional Networks
W Jin, T Derr, Y Wang, Y Ma, Z Liu, J Tang
International Conference on Web Search and Data Mining (WSDM), 2021
Self-supervised learning on graphs: Deep insights and new direction
W Jin, T Derr, H Liu, Y Wang, S Wang, Z Liu, J Tang
The Web Conference (WWW 2021) Workshop: Self-Supervised Learning for the Web, 2021
Deeprobust: a platform for adversarial attacks and defenses
Y Li*, W Jin*, H Xu, J Tang
Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 16078 …, 2021
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
L Zhao, W Jin, L Akoglu, N Shah
International Conference on Learning Representations (ICLR), 2022
Elastic graph neural networks
X Liu*, W Jin*, Y Ma, Y Li, H Liu, Y Wang, M Yan, J Tang
International Conference on Machine Learning (ICML), 6837-6849, 2021
Graph Condensation for Graph Neural Networks
W Jin, L Zhao, S Zhang, Y Liu, J Tang, N Shah
International Conference on Learning Representations (ICLR), 2022
Graph Data Augmentation for Graph Machine Learning: A Survey
T Zhao, W Jin, Y Wang, G Liu, Y Liu, S Gunnemann, N Shah, M Jiang
IEEE Data Engineering Bulletin (DEBULL), 2023
Exploring the potential of large language models (llms) in learning on graphs
Z Chen, H Mao, H Li, W Jin, H Wen, X Wei, S Wang, D Yin, W Fan, H Liu, ...
SIGKDD Explorations, 2023
Graph trend filtering networks for recommendation
W Fan, X Liu, W Jin, X Zhao, J Tang, Q Li
Proceedings of the 45th international ACM SIGIR conference on research and …, 2022
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels
E Dai, W Jin, H Liu, S Wang
WSDM 2022, 2022
Automated self-supervised learning for graphs
W Jin, X Liu, X Zhao, Y Ma, N Shah, J Tang
International Conference on Learning Representations (ICLR), 2022
Condensing Graphs via One-Step Gradient Matching
W Jin, X Tang, H Jiang, Z Li, D Zhang, J Tang, B Yin
KDD 2022, 2022
Empowering graph representation learning with test-time graph transformation
W Jin, T Zhao, J Ding, Y Liu, J Tang, N Shah
ICLR 2023, 2022
Graph Neural Networks for Multimodal Single-Cell Data Integration
H Wen*, J Ding*, W Jin*, Y Wang*, Y Xie, J Tang
KDD 2022, 2022
Graph neural networks with adaptive residual
X Liu, J Ding, W Jin, H Xu, Y Ma, Z Liu, J Tang
NeurIPS 2021, 2021
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
W Jin, X Liu, Y Ma, C Aggarwal, J Tang
KDD 2022, 2022
Graph neural networks: Self-supervised learning
Y Wang, W Jin, T Derr
Graph Neural Networks: Foundations, Frontiers, and Applications, 391-420, 2022
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Articles 1–20