Chuan Shi

papers

593

total citations

papers (18)

Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure

NEURIPS 2022arXiv

137

citations

Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration

NEURIPS 2021arXiv

134

citations

Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum

NEURIPS 2022arXiv

citations

Uncovering the Structural Fairness in Graph Contrastive Learning

NEURIPS 2022arXiv

citations

Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization

AAAI 2024arXiv

citations

FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization

AAAI 2024arXiv

citations

A Generalized Neural Diffusion Framework on Graphs

AAAI 2024arXiv

citations

Graph Contrastive Invariant Learning from the Causal Perspective

AAAI 2024arXiv

citations

Learning Invariant Representations of Graph Neural Networks via Cluster Generalization

NEURIPS 2023arXiv

citations

Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective

AAAI 2025arXiv

citations

Blend the Separated: Mixture of Synergistic Experts for Data-Scarcity Drug-Target Interaction Prediction

AAAI 2025arXiv

citations

Harnessing Language Model for Cross-Heterogeneity Graph Knowledge Transfer

AAAI 2025

citations

Less is More: on the Over-Globalizing Problem in Graph Transformers

ICML 2024arXiv

citations

Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization

NEURIPS 2023

citations

Graph Contrastive Learning with Stable and Scalable Spectral Encoding

NEURIPS 2023

citations

Chuan Shi

papers (18)

Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure

Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration

Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum

Uncovering the Structural Fairness in Graph Contrastive Learning

Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization

FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization

A Generalized Neural Diffusion Framework on Graphs

Graph Contrastive Invariant Learning from the Causal Perspective

Learning Invariant Representations of Graph Neural Networks via Cluster Generalization

Provable Training for Graph Contrastive Learning

Graph Distillation with Eigenbasis Matching

Federated Graph Condensation with Information Bottleneck Principles

Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective

Blend the Separated: Mixture of Synergistic Experts for Data-Scarcity Drug-Target Interaction Prediction

Harnessing Language Model for Cross-Heterogeneity Graph Knowledge Transfer

Less is More: on the Over-Globalizing Problem in Graph Transformers

Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization

Graph Contrastive Learning with Stable and Scalable Spectral Encoding

papers (18)

Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure

Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration

Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum

Uncovering the Structural Fairness in Graph Contrastive Learning

Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization

FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization

A Generalized Neural Diffusion Framework on Graphs

Graph Contrastive Invariant Learning from the Causal Perspective

Learning Invariant Representations of Graph Neural Networks via Cluster Generalization

Provable Training for Graph Contrastive Learning

Graph Distillation with Eigenbasis Matching

Federated Graph Condensation with Information Bottleneck Principles

Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective

Blend the Separated: Mixture of Synergistic Experts for Data-Scarcity Drug-Target Interaction Prediction

Harnessing Language Model for Cross-Heterogeneity Graph Knowledge Transfer

Less is More: on the Over-Globalizing Problem in Graph Transformers

Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization

Graph Contrastive Learning with Stable and Scalable Spectral Encoding