Chuang Liu

Orcid: 0000-0003-2377-2567

Affiliations:
  • Wuhan University, China


According to our database1, Chuang Liu authored at least 16 papers between 2021 and 2024.

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Bibliography

2024
Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., October, 2024

Exploring sparsity in graph transformers.
Neural Networks, 2024

Towards a better negative sampling strategy for dynamic graphs.
Neural Networks, 2024

Dual-perspective Cross Contrastive Learning in Graph Transformers.
CoRR, 2024

Hi-GMAE: Hierarchical Graph Masked Autoencoders.
CoRR, 2024

Gradformer: Graph Transformer with Exponential Decay.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

A Debiased Graph Clustering Approach Using Dual Contrastive Learning.
Proceedings of the IEEE International Conference on Web Services, 2024

2023
On exploring node-feature and graph-structure diversities for node drop graph pooling.
Neural Networks, October, 2023

Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes.
CoRR, 2023

Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Gapformer: Graph Transformer with Graph Pooling for Node Classification.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

2022
Vega-MT: The JD Explore Academy Translation System for WMT22.
CoRR, 2022

Vega-MT: The JD Explore Academy Machine Translation System for WMT22.
Proceedings of the Seventh Conference on Machine Translation, 2022

Masked Graph Auto-Encoder Constrained Graph Pooling.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

2021
Enhancing Graph Neural Networks by a High-quality Aggregation of Beneficial Information.
Neural Networks, 2021


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