Sitao Luan

According to our database¹, Sitao Luan authored at least 24 papers between 2019 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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Links

On csauthors.net:

Bibliography

2024

Rethinking Structure Learning For Graph Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2024

Is Graph Convolution Always Beneficial For Every Feature?

[BibT_eX]

[DOI]

CoRR, 2024

EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics.

[BibT_eX]

[DOI]

CoRR, 2024

Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Are Heterophily-Specific GNNs and Homophily Metrics Really Effective? Evaluation Pitfalls and New Benchmarks.

[BibT_eX]

[DOI]

CoRR, 2024

Reactzyme: A Benchmark for Enzyme-Reaction Prediction.

[BibT_eX]

[DOI]

CoRR, 2024

The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges.

[BibT_eX]

[DOI]

CoRR, 2024

What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks.

[BibT_eX]

[DOI]

Yilun Zheng

Sitao Luan

Lihui Chen

CoRR, 2024

GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection.

[BibT_eX]

[DOI]

Qincheng Lu

Sitao Luan

Xiao-Wen Chang

CoRR, 2024

Representation Learning on Heterophilic Graph with Directional Neighborhood Attention.

[BibT_eX]

[DOI]

CoRR, 2024

2023

On Addressing the Limitations of Graph Neural Networks.

[BibT_eX]

[DOI]

Sitao Luan

CoRR, 2023

When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

MUDiff: Unified Diffusion for Complete Molecule Generation.

[BibT_eX]

[DOI]

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Complex Networks & Their Applications XII, 2023

When Do We Need Graph Neural Networks for Node Classification?

[BibT_eX]

[DOI]

Proceedings of the Complex Networks & Their Applications XII, 2023

2022

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2022

When Do We Need GNN for Node Classification?

[BibT_eX]

[DOI]

CoRR, 2022

Revisiting Heterophily For Graph Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?

[BibT_eX]

[DOI]

CoRR, 2021

A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks.

[BibT_eX]

[DOI]

CoRR, 2020

META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation.

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019

Revisit Policy Optimization in Matrix Form.

[BibT_eX]

[DOI]

Sitao Luan

Xiao-Wen Chang

Doina Precup

CoRR, 2019

Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Sitao Luan

Timeline

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Bibliography

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