Suhang Wang

发表

Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks

Xiang Zhang, Tianxiang Zhao, Suhang Wang, 2022, ArXiv.

Decoupled Self-supervised Learning for Non-Homophilous Graphs

Zhimeng Guo, Suhang Wang, Teng Xiao, 2022, ArXiv.

Towards Bridging Algorithm and Theory for Unbiased Recommendation

Suhang Wang, Teng Xiao, Zhengyu Chen, 2022, ArXiv.

On Consistency in Graph Neural Network Interpretation

Xiang Zhang, Tianxiang Zhao, Suhang Wang, 2022, ArXiv.

Link Prediction on Heterophilic Graphs via Disentangled Representation Learning

C. Aggarwal, Xiang Zhang, Zhimeng Guo, 2022, ArXiv.

Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels

Hui Liu, Enyan Dai, Suhang Wang, 2022, WSDM.

Representation Matters When Learning From Biased Feedback in Recommendation

Suhang Wang, Teng Xiao, Zhengyu Chen, 2022, CIKM.

Towards Prototype-Based Self-Explainable Graph Neural Network

Enyan Dai, Suhang Wang, 2022, ArXiv.

Jointly Attacking Graph Neural Network and its Explanations

Jiliang Tang, C. Aggarwal, Wei Jin, 2021, 2023 IEEE 39th International Conference on Data Engineering (ICDE).

TopoImb: Toward Topology-level Imbalance in Learning from Graphs

Xiang Zhang, Tianxiang Zhao, Suhang Wang, 2022, LoG.

Towards Faithful and Consistent Explanations for Graph Neural Networks

Xiang Zhang, Tianxiang Zhao, Suhang Wang, 2022, WSDM.

Decoupled Self-supervised Learning for Graphs

Zhimeng Guo, Suhang Wang, Teng Xiao, 2022, NeurIPS.

Learning Fair Graph Neural Networks With Limited and Private Sensitive Attribute Information

Enyan Dai, Suhang Wang, 2023, IEEE Transactions on Knowledge and Data Engineering.

GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

Xiang Zhang, Tianxiang Zhao, Suhang Wang, 2021, WSDM.

Graph Adversarial Attack via Rewiring

Jiliang Tang, Yao Ma, Lingfei Wu, 2021, KDD.

Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment

Xiang Zhang, Tianxiang Zhao, Suhang Wang, 2023, ACM Transactions on Intelligent Systems and Technology.

Counterfactual Learning on Graphs: A Survey

Hui Liu, C. Aggarwal, Zhimeng Guo, 2023, ArXiv.

HP-GMN: Graph Memory Networks for Heterophilous Graphs

Xiang Zhang, Enyan Dai, Suhang Wang, 2022, 2022 IEEE International Conference on Data Mining (ICDM).

Exploring Edge Disentanglement for Node Classification

Xiang Zhang, Tianxiang Zhao, Suhang Wang, 2022, WWW.

Towards Self-Explainable Graph Neural Network

Enyan Dai, Suhang Wang, Enyan Dai, 2021, CIKM.

Learning How to Propagate Messages in Graph Neural Networks

Donglin Wang, Suhang Wang, Teng Xiao, 2021, KDD.

Friend Story Ranking with Edge-Contextual Local Graph Convolutions

Neil Shah, Yozen Liu, Xianfeng Tang, 2022, WSDM.

Learning Fair Models without Sensitive Attributes: A Generative Approach

Suhang Wang, Huaisheng Zhu, 2022, ArXiv.

NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs

Charu Aggarwal, Enyan Dai, Suhang Wang, 2021, KDD.

Unnoticeable Backdoor Attacks on Graph Neural Networks

Xiang Zhang, Enyan Dai, Suhang Wang, 2023, WWW.

Label-Wise Message Passing Graph Neural Network on Heterophilic Graphs pdf

Enyan Dai, Suhang Wang, Zhimeng Guo, 2021, ArXiv.

Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features

Kai Shu, Enyan Dai, Suhang Wang, 2021, WSDM.

Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network pdf

Enyan Dai, Tianxiang Zhao, Suhang Wang, 2021, Applied Energy.

Semi-supervised anomaly detection in dynamic communication networks

Xuying Meng, Yujun Zhang, Di Yao, 2021, Inf. Sci..

Graph Routing between Capsules

Wei Zhao, Erik Cambria, Steffen Eger, 2021, Neural Networks.

Adaptive Structural Co-regularization for Unsupervised Multi-view Feature Selection

Vasant Honavar, Suhang Wang, Tsung-Yu Hsieh, 2019, 2019 IEEE International Conference on Big Knowledge (ICBK).

Integrating Multimodal and Longitudinal Neuroimaging Data with Multi-Source Network Representation Learning

Yalin Wang, Huan Liu, Kai Shu, 2021, Neuroinformatics.

Fairness-aware Message Passing for Graph Neural Networks

Zhimeng Guo, Zhiwei Zhang, Suhang Wang, 2023, ArXiv.

Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations

Wei Cheng, Yanchi Liu, Wenchao Yu, 2023, KDD.

Semi-Supervised Graph-to-Graph Translation

Xiang Zhang, Xianfeng Tang, Tianxiang Zhao, 2020, CIKM.

SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series

Xianfeng Tang, Vasant G. Honavar, Yiwei Sun, 2021, WWW.

Label-Wise Graph Convolutional Network for Heterophilic Graphs

Zhimeng Guo, Enyan Dai, Suhang Wang, 2021, LoG.

Interactive Anomaly Detection in Dynamic Communication Networks

Xuying Meng, Yujun Zhang, Di Yao, 2021, IEEE/ACM Transactions on Networking.

Learning Latent Perception Graphs for Personalized Unknowns Recommendation

Huan Liu, Shan-Yun Teng, Suhang Wang, 2020, 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI).

Self-Explainable Graph Neural Networks for Link Prediction

Xianfeng Tang, Suhang Wang, Dongsheng Luo, 2023, ArXiv.

Functional Autoencoders for Functional Data Representation Learning

Vasant Honavar, Yiwei Sun, Suhang Wang, 2021, SDM.

Towards Off-Policy Learning for Ranking Policies with Logged Feedback

Suhang Wang, Teng Xiao, 2022, AAAI Conference on Artificial Intelligence.

SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Time Series Data

Vasant G Honavar, Yiwei Sun, Xianfeng Tang, 2021 .

Neural Utility Functions

Huaxiu Yao, Porter Jenkins, Ahmad Farag, 2021, AAAI.

A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy

Zhengyang Wang, Xianfeng Tang, Limeng Cui, 2023, Knowledge Discovery and Data Mining.

Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment

Xiang Zhang, Tianxiang Zhao, Suhang Wang, 2023, ACM Trans. Intell. Syst. Technol..

Towards Fair Graph Neural Networks via Graph Counterfactual

Yao Ma, Zhimeng Guo, Suhang Wang, 2023, CIKM.

Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations

Wei Cheng, Yanchi Liu, Wenchao Yu, 2023, KDD.

Disambiguated Node Classification with Graph Neural Networks

Tianxiang Zhao, Suhang Wang, Xiang Zhang, 2024, 2402.08824.

Learning Graph Filters for Spectral GNNs via Newton Interpolation

Enyan Dai, Suhang Wang, Dongsheng Luo, 2023, ArXiv.

Active Learning for Graphs with Noisy Structures

Yao Ma, Suhang Wang, Hongliang Chi, 2024, arXiv.org.

Learning How to Propagate Messages in Graph Neural Networks

Teng Xiao, Suhang Wang, Donglin Wang, 2021, KDD.

Shape-aware Graph Spectral Learning

Enyan Dai, Suhang Wang, Dongsheng Luo, 2023, 2310.10064.

Decoupled Self-supervised Learning for Non-Homophilous Graphs

Zhimeng Guo, Suhang Wang, Teng Xiao, 2022, ArXiv.

Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective

Suhang Wang, Teng Xiao, Zhengyu Chen, 2023, KDD.