@inproceedings{zhang2024disen,title={Disentangled Dynamic Graph Attention Network for Out-of-distribution Sequential Recommendation},author={Zhang, Zeyang and Wang, Xin and Chen, Haibo and Li, Haoyang and Zhu, Wenwu},booktitle={ACM TOIS},year={2024},topic={ood},}
KDD’24
[23] LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?
@inproceedings{zhang2023LLM4DyG,title={LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?},author={Zhang, Zeyang and Wang, Xin and Zhang, Ziwei and Li, Haoyang and Qin, Yijian and Zhu, Wenwu},booktitle={ACM SIGKDD},year={2024},topic={llm},}
@inproceedings{gasim,title={Disentangled Continual Graph Neural Architecture Search with Invariant Modular Supernet},author={Zhang, Zeyang and Wang, Xin and Qin, Yijian and Chen, Hong and Chu, Xu and Zhang, Ziwei and Zhu, Wenwu},booktitle={ICML},topic={auto},year={2024},}
ICML’24
[21] Disentangled Graph Self-supervised Learning for Out-of-distribution Generalization
@inproceedings{li2024dis,title={Disentangled Graph Self-supervised Learning for Out-of-distribution Generalization},author={Li, Haoyang and Wang, Xin and Zhang, Zeyang and Zhang, Haibo Chen Ziwei and Zhu, Wenwu},booktitle={ICML},year={2024}}
KDD’24
[20] Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
@inproceedings{xie2024towards,title={Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification},author={Xie, Beini and Chang, Heng and Zhang, Ziwei and Zhang, Zeyang and Wu, Simin and Wang, Xin and Meng, Yuan and Zhu, Wenwu},booktitle={ACM SIGKDD},year={2024},}
NeurIPS’24
[19] VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding
Houlun Chen, Xin Wang, Hong Chen, Zeyang Zhang, Wei Feng, Bin Huang, Jia Jia, and Wenwu Zhu
@article{chen2024verified,title={VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding},author={Chen, Houlun and Wang, Xin and Chen, Hong and Zhang, Zeyang and Feng, Wei and Huang, Bin and Jia, Jia and Zhu, Wenwu},journal={NeurIPS},year={2024},}
IJCAI’24t
[18] Graph Machine Learning under Distribution Shifts: Adaptation, Generalization and Extension to LLMs
@article{xin2024graph,title={Graph Machine Learning under Distribution Shifts: Adaptation, Generalization and Extension to LLMs},author={Wang, Xin and Li, Haoyang and Zhang, Zeyang and Zhu, Wenwu},journal={IJCAI tutorial},year={2024},}
Arxiv
[17] LLM-Enhanced Causal Discovery in Temporal Domain from Interventional Data
Peiwen Li, Xin Wang, Zeyang Zhang, Yuan Meng, Fang Shen, Yue Li, Jialong Wang, Yang Li, and Wenweu Zhu
@misc{li2024llmenhanced,title={LLM-Enhanced Causal Discovery in Temporal Domain from Interventional Data},author={Li, Peiwen and Wang, Xin and Zhang, Zeyang and Meng, Yuan and Shen, Fang and Li, Yue and Wang, Jialong and Li, Yang and Zhu, Wenweu},year={2024},eprint={2404.14786},archiveprefix={arXiv},primaryclass={cs.AI},}
BioRxiv
[16] Decoding cell identity with multi-scale explainable deep learning
Jun Zhu*, Zeyang Zhang*, Yujia Xiang, Beini Xie, Xinwen Dong, Linhai Xie, Peijie Zhou, Rongyan Yao, Xiaowen Wang, Yang Li, Fuchu He, Wenwu Zhu, Ziwei Zhang, and Cheng Chang
Cells are the fundamental structural and functional units of life. Studying the definition and composition of different cell types can help us understand the complex mechanisms underlying biological diversity and functionality. The increasing volume of extensive single-cell omics data makes it possible to provide detailed characterisations of cell types. Recently, there has been a rise in deep learning-based approaches that generate cell type labels solely through mapping query data to reference data. However, these approaches lack multi-scale descriptions and interpretations of identified cell types. Here, we propose Cell Decoder, a biological prior knowledge informed model to achieve multi-scale representation of cells. We implemented automated machine learning and post-hoc analysis techniques to decode cell identity. We have shown that Cell Decoder compares favourably to existing methods, offering multi-view interpretability for decoding cell identity and data integration. Furthermore, we have showcased its applicability in uncovering novel cell types and states in both human bone and mouse embryonic contexts, thereby revealing the multi-scale heterogeneity inherent in cell identities.Competing Interest StatementThe authors have declared no competing interest.
@article{Zhu2024.02.05.578922,author={Zhu*, Jun and Zhang*, Zeyang and Xiang, Yujia and Xie, Beini and Dong, Xinwen and Xie, Linhai and Zhou, Peijie and Yao, Rongyan and Wang, Xiaowen and Li, Yang and He, Fuchu and Zhu, Wenwu and Zhang, Ziwei and Chang, Cheng},title={Decoding cell identity with multi-scale explainable deep learning},elocation-id={2024.02.05.578922},year={2024},doi={10.1101/2024.02.05.578922},publisher={Cold Spring Harbor Laboratory},eprint={https://www.biorxiv.org/content/early/2024/02/08/2024.02.05.578922.full.pdf},journal={bioRxiv},topic={ai4science},}
Arxiv
[15] Exploring the Potential of Large Language Models in Graph Generation
@article{yao2024exploring,title={Exploring the Potential of Large Language Models in Graph Generation},author={Yao, Yang and Wang, Xin and Zhang, Zeyang and Qin, Yijian and Zhang, Ziwei and Chu, Xu and Yang, Yuekui and Zhu, Wenwu and Mei, Hong},journal={arXiv preprint arXiv:2403.14358},year={2024},topic={llm},}
@inproceedings{zhang2023large,title={Graph Meets LLMs: Towards Large Graph Models},author={Zhang, Ziwei and Li, Haoyang and Zhang, Zeyang and Qin, Yijian and Wang, Xin and Zhu, Wenwu},booktitle={Conference on Neural Information Processing Systems Workshop},year={2023},topic={llm},}
Arxiv
[13] LLM4VG: Large Language Models Evaluation for Video Grounding
Wei Feng, Xin Wang, Hong Chen, Zeyang Zhang, Zihan Song, Yuwei Zhou, and Wenwu Zhu
@article{feng2023llm4vg,title={LLM4VG: Large Language Models Evaluation for Video Grounding},author={Feng, Wei and Wang, Xin and Chen, Hong and Zhang, Zeyang and Song, Zihan and Zhou, Yuwei and Zhu, Wenwu},journal={arXiv preprint arXiv:2312.14206},year={2023},}
Arxiv
[12] Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion
@article{zhang2023outofdistribution,title={Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion},author={Zhang, Zeyang and Wang, Xin and Zhang, Ziwei and Li, Haoyang and Zhu, Wenwu},year={2023},journal={arXiv preprint arXiv:2311.14255},}
MedAI’23
[11] Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction
Zeyang Zhang*, Xingwang Li*, Fei Teng, Ning Lin, Xueling Zhu, Xin Wang, and Wenwu Zhu
In IEEE International Conference on Medical Artificial Intelligence, 2023
@inproceedings{zhang2023ood,title={Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction},author={Zhang*, Zeyang and Li*, Xingwang and Teng, Fei and Lin, Ning and Zhu, Xueling and Wang, Xin and Zhu, Wenwu},booktitle={IEEE International Conference on Medical Artificial Intelligence},year={2023},topic={ai4science},}
NeurIPS’23
[10] Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision
@inproceedings{zhang2023unsupervised,title={Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision},author={Zhang, Zeyang and Wang, Xin and Zhang, Ziwei and Shen, Guangyao and Shen, Shiqi and Zhu, Wenwu},booktitle={Advances in Neural Information Processing Systems},year={2023},topic={auto},}
NeurIPS’23
[9] Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
@inproceedings{zhang2023spectral,title={Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts},author={Zhang, Zeyang and Wang, Xin and Zhang, Ziwei and Qin, Zhou and Wen, Weigao and Xue, Hui and Li, Haoyang and Zhu, Wenwu},booktitle={Advances in Neural Information Processing Systems},year={2023},topic={ood},}
@inproceedings{zhang2023dynamic,title={Dynamic Heterogeneous Graph Attention Neural Architecture Search},author={Zhang, Zeyang and Zhang, Ziwei and Wang, Xin and Qin, Yijian and Qin, Zhou and Zhu, Wenwu},booktitle={Thirty-Seventh {AAAI} Conference on Artificial Intelligence},year={2023},topic={auto},}
2022
NeurIPS’22
[7] Dynamic graph neural networks under spatio-temporal distribution shift
@inproceedings{zhang2022dynamic,title={Dynamic graph neural networks under spatio-temporal distribution shift},author={Zhang, Zeyang and Wang, Xin and Zhang, Ziwei and Li, Haoyang and Qin, Zhou and Zhu, Wenwu},booktitle={Advances in Neural Information Processing Systems},year={2022},topic={ood},}
AAAI’22
[6] Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum
@article{zhang2022learning,title={Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum},author={Zhang, Zeyang and Zhang, Ziwei and Wang, Xin and Zhu, Wenwu},journal={Thirty-Fifth {AAAI} Conference on Artificial Intelligence},year={2022},topic={ai4science},}
@inproceedings{qin2022bench,title={NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search},author={Qin, Yijian and Zhang, Ziwei and Wang, Xin and Zhang, Zeyang and Zhu, Wenwu},booktitle={Thirty-Sixth Conference on Neural Information Processing Systems},year={2022},}
2021
CVPR’21w
[4] One-Shot Neural Channel Search: What Works and What’s Next
@inproceedings{guan2021one,title={One-Shot Neural Channel Search: What Works and What's Next},author={Guan, Chaoyu and Qin, Yijian and Wei, Zhikun and Zhang, Zeyang and Zhang, Zizhao and Wang, Xin and Zhu, Wenwu},booktitle={CVPR 2021 workshop},year={2021},}
ICLR’21w
[3] AutoGL: A Library for Automated Graph Learning
@inproceedings{guan2021autogl,title={AutoGL: A Library for Automated Graph Learning},author={Guan, Chaoyu and Zhang, Ziwei and Li, Haoyang and Chang, Heng and Zhang, Zeyang and Qin, Yijian and Jiang, Jiyan and Wang, Xin and Zhu, Wenwu},booktitle={ICLR 2021 Workshop GTRL},year={2021},}
NeurIPS’21
[2] Graph differentiable architecture search with structure learning
@inproceedings{qin2021graph,title={Graph differentiable architecture search with structure learning},author={Qin, Yijian and Wang, Xin and Zhang, Zeyang and Zhu, Wenwu},booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},year={2021},}
Arxiv
[1] Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?
@article{zhang2021revisiting,title={Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?},author={Zhang, Ziwei and Wang, Xin and Zhang, Zeyang and Cui, Peng and Zhu, Wenwu},journal={arXiv preprint arXiv:2112.12345},year={2021},}
[23] LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?In ACM SIGKDD, 2024
[22] Disentangled Continual Graph Neural Architecture Search with Invariant Modular SupernetIn ICML, 2024
[15] Exploring the Potential of Large Language Models in Graph GenerationarXiv preprint arXiv:2403.14358, 2024
