Publications

Listed by categories in reversed chronological order, where + indicates equal contribution and * denotes corresponding author.

I have published some papers in refereed journals and conferences, including DM venues (e.g., KDD*7, WWW*4, ICDE*1, TKDE*4), AI venues (e.g., NeurIPS*3, IJCAI*4, AAAI*2, ICLR*1, AI*1), and CV venues (e.g., ECCV*1, MM*2). Here are some representative papers:

Spatio-Temporal (ST) Data Mining:
- ST graph forecasting: [TKDE’22], [SIGSPATIAL’22], [TKDE’20], [KDD’19]
- Trajectory modeling: [ICDE’23], [CIKM’22], [IJCAI’21]
- Urban flow analytics: [WWW’21], [TKDE’20], [TKDE’20], [KDD’19]
- AutoML for ST data: [AI’23], [WWW’21], [KDD’21]
- Applications: [AAAI’23], [UBICOMP’21], [TVCG’21], [AAAI’20], [IJCAI’18], [SIGSPATIAL’17], [IJCAI’16]
Graph Mining:
- Graph learning: [ICLR’23], [WWW’21], [NeurIPS’21], [NeurIPS’20]
- Graph augmentation: [WWW’21], [NeurIPS’21], [KDD’20]
- Applications: [KDD’22]
Computer Vision: [ECCV’22], [MM’22], [MM’21].

2023

KDD’23

[New] Graph Neural Processes for Spatio-Temporal Extrapolation

Junfeng Hu, Yuxuan Liang*, Zhencheng Fan, Hongyang Chen, Yu Zheng, and Roger Zimmermann

In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

@inproceedings{hu2023graph,
  title = {Graph Neural Processes for Spatio-Temporal Extrapolation},
  author = {Hu, Junfeng and Liang*, Yuxuan and Fan, Zhencheng and Chen, Hongyang and Zheng, Yu and Zimmermann, Roger},
  booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year = {2023}
}

KDD’23

[New] Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning

Zhengyang Zhou, Qihe Huang, Kuo Yang, Kun Wang, Xu Wang, Yudong Zhang, Yuxuan Liang, and Yang Wang

In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

@inproceedings{zhou2023maintaining,
  title = {Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning},
  author = {Zhou, Zhengyang and Huang, Qihe and Yang, Kuo and Wang, Kun and Wang, Xu and Zhang, Yudong and Liang, Yuxuan and Wang, Yang},
  booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year = {2023}
}

Arxiv

[New] Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey

Guangyin Jin, Yuxuan Liang, Yuchen Fang, Jincai Huang, Junbo Zhang, and Yu Zheng

arXiv preprint arXiv:2303.14483 2023
AI

[New] AutoSTG+: An Automatic Framework to Discover The Optimal Network for Spatio-temporal Graph Prediction

Songyu Ke, Zheyi Pan, Tianfu He, Yuxuan Liang, Junbo Zhang, and Yu Zheng

Artificial Intelligence 2023

ICDE’23

[New] Contrastive Trajectory Similarity Learning with Dual-Feature Attention

Yanchuan Chang, Jianzhong Qi, Yuxuan Liang, and Egemen Tanin

In The 39th IEEE International Conference on Data Engineering, 2023

@inproceedings{chang2023traj,
  title = {Contrastive Trajectory Similarity Learning with Dual-Feature Attention},
  author = {Chang, Yanchuan and Qi, Jianzhong and Liang, Yuxuan and Tanin, Egemen},
  booktitle = {The 39th IEEE International Conference on Data Engineering},
  year = {2023}
}

ICLR’23

Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective

Kun Wang, Yuxuan Liang*, Pengkun Wang, Xu Wang, Pengfei Gu, Junfeng Fang, and Yang Wang*

In The International Conference on Learning Representations, 2023

@inproceedings{wang2023searching,
  title = {Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective},
  author = {Wang, Kun and Liang*, Yuxuan and Wang, Pengkun and Wang, Xu and Gu, Pengfei and Fang, Junfeng and Wang*, Yang},
  booktitle = {The International Conference on Learning Representations},
  year = {2023}
}

AAAI’23

AirFormer: Predicting Nationwide Air Quality in China with Transformers

Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, and Roger Zimmermann

In Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

@inproceedings{liang2023airformer,
  title = {AirFormer: Predicting Nationwide Air Quality in China with Transformers},
  author = {Liang, Yuxuan and Xia, Yutong and Ke, Songyu and Wang, Yiwei and Wen, Qingsong and Zhang, Junbo and Zheng, Yu and Zimmermann, Roger},
  booktitle = {Thirty-Seventh AAAI Conference on Artificial Intelligence},
  year = {2023}
}

2022

Mixed-Order Relation-Aware Recurrent Neural Networks for Spatio-Temporal Forecasting

Yuxuan Liang, Kun Ouyang, Yiwei Wang, Zheyi Pan, Yifang Yin, Hongyang Chen, Junbo Zhang, Yu Zheng, David S Rosenblum, and Roger Zimmermann

IEEE Transactions on Knowledge and Data Engineering 2022

@article{liang2022mixed,
  title = {Mixed-Order Relation-Aware Recurrent Neural Networks for Spatio-Temporal Forecasting},
  author = {Liang, Yuxuan and Ouyang, Kun and Wang, Yiwei and Pan, Zheyi and Yin, Yifang and Chen, Hongyang and Zhang, Junbo and Zheng, Yu and Rosenblum, David S and Zimmermann, Roger},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2022},
  publisher = {IEEE}
}

SIGSPATIAL’22

When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?

Xu Liu+, Yuxuan Liang+, Chao Huang, Yu Zheng, Bryan Hooi, and Roger Zimmermann

In Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022

@inproceedings{liu2022contrastive,
  title = {When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?},
  author = {Liu+, Xu and Liang+, Yuxuan and Huang, Chao and Zheng, Yu and Hooi, Bryan and Zimmermann, Roger},
  booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
  pages = {1--12},
  year = {2022}
}

ECCV’22

Dualformer: Local-global stratified transformer for efficient video recognition

Yuxuan Liang, Pan Zhou, Roger Zimmermann, and Shuicheng Yan

In European Conference on Computer Vision, 2022

@inproceedings{liang2022dualformer,
  title = {Dualformer: Local-global stratified transformer for efficient video recognition},
  author = {Liang, Yuxuan and Zhou, Pan and Zimmermann, Roger and Yan, Shuicheng},
  booktitle = {European Conference on Computer Vision},
  pages = {577--595},
  year = {2022},
  organization = {Springer}
}

CIKM’22

TrajFormer: Efficient Trajectory Classification with Transformers

Yuxuan Liang, Kun Ouyang, Yiwei Wang, Xu Liu, Hongyang Chen, Junbo Zhang, Yu Zheng, and Roger Zimmermann

In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

@inproceedings{liang2022trajformer,
  title = {TrajFormer: Efficient Trajectory Classification with Transformers},
  author = {Liang, Yuxuan and Ouyang, Kun and Wang, Yiwei and Liu, Xu and Chen, Hongyang and Zhang, Junbo and Zheng, Yu and Zimmermann, Roger},
  booktitle = {Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
  pages = {1229--1237},
  year = {2022}
}

SIGSPATIAL’22

Periodic Residual Learning for Crowd Flow Forecasting

Chengxin Wang, Yuxuan Liang, and Gary Tan

In Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022

@inproceedings{wang2022periodic,
  title = {Periodic Residual Learning for Crowd Flow Forecasting},
  author = {Wang, Chengxin and Liang, Yuxuan and Tan, Gary},
  booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
  pages = {1--10},
  year = {2022}
}

KDD’22

Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation

Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, and Chenliang Li

In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022

@inproceedings{yang2022multi,
  title = {Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation},
  author = {Yang, Yuhao and Huang, Chao and Xia, Lianghao and Liang, Yuxuan and Yu, Yanwei and Li, Chenliang},
  booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages = {2263--2274},
  year = {2022}
}

MM’22

Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery

Wenmiao Hu, Yichen Zhang, Yuxuan Liang, Yifang Yin, Andrei Georgescu, An Tran, Hannes Kruppa, See-Kiong Ng, and Roger Zimmermann

In Proceedings of the 30th ACM International Conference on Multimedia, 2022

@inproceedings{hu2022beyond,
  title = {Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery},
  author = {Hu, Wenmiao and Zhang, Yichen and Liang, Yuxuan and Yin, Yifang and Georgescu, Andrei and Tran, An and Kruppa, Hannes and Ng, See-Kiong and Zimmermann, Roger},
  booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
  pages = {6155--6164},
  year = {2022}
}

Access

Content-Attribute Disentanglement for Generalized Zero-Shot Learning

Yoojin An, Sangyeon Kim, Yuxuan Liang, Roger Zimmermann, Dongho Kim, and Jihie Kim

IEEE Access 2022

IJCNN’22

Time-Aware Neighbor Sampling on Temporal Graphs

Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, and Bryan Hooi

In 2022 International Joint Conference on Neural Networks (IJCNN), 2022

@inproceedings{wang2022time,
  title = {Time-Aware Neighbor Sampling on Temporal Graphs},
  author = {Wang, Yiwei and Cai, Yujun and Liang, Yuxuan and Ding, Henghui and Wang, Changhu and Hooi, Bryan},
  booktitle = {2022 International Joint Conference on Neural Networks (IJCNN)},
  pages = {1--8},
  year = {2022},
  organization = {IEEE}
}

NAACL’22

Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis

Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, and Bryan Hooi

In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

@inproceedings{wang-etal-2022-rely,
  title = {Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis},
  author = {Wang, Yiwei and Chen, Muhao and Zhou, Wenxuan and Cai, Yujun and Liang, Yuxuan and Liu, Dayiheng and Yang, Baosong and Liu, Juncheng and Hooi, Bryan},
  booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2022},
  publisher = {Association for Computational Linguistics},
  pages = {3071--3081}
}

2021

IJCAI’21

Modeling Trajectories with Neural Ordinary Differential Equations.

Yuxuan Liang, Kun Ouyang, Hanshu Yan, Yiwei Wang, Zekun Tong, and Roger Zimmermann

In International Joint Conference on Artificial Intelligence, 2021

@inproceedings{liang2021modeling,
  title = {Modeling Trajectories with Neural Ordinary Differential Equations.},
  author = {Liang, Yuxuan and Ouyang, Kun and Yan, Hanshu and Wang, Yiwei and Tong, Zekun and Zimmermann, Roger},
  booktitle = {International Joint Conference on Artificial Intelligence},
  pages = {1498--1504},
  year = {2021}
}

WWW’21

Fine-grained Urban Flow Prediction

Yuxuan Liang, Kun Ouyang, Junkai Sun, Yiwei Wang, Junbo Zhang, Yu Zheng, David Rosenblum, and Roger Zimmermann

In Proceedings of the Web Conference, 2021

Bib PDF Cited by 32

@inproceedings{liang2021fine,
  title = {Fine-grained Urban Flow Prediction},
  author = {Liang, Yuxuan and Ouyang, Kun and Sun, Junkai and Wang, Yiwei and Zhang, Junbo and Zheng, Yu and Rosenblum, David and Zimmermann, Roger},
  booktitle = {Proceedings of the Web Conference},
  pages = {1833--1845},
  year = {2021}
}

WWW’21

AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph

Zheyi Pan, Songyu Ke, Xiaodu Yang, Yuxuan Liang, Yong Yu, Junbo Zhang, and Yu Zheng

In Proceedings of the Web Conference, 2021

@inproceedings{pan2021autostg,
  title = {AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph},
  author = {Pan, Zheyi and Ke, Songyu and Yang, Xiaodu and Liang, Yuxuan and Yu, Yong and Zhang, Junbo and Zheng, Yu},
  booktitle = {Proceedings of the Web Conference},
  pages = {1846--1855},
  year = {2021}
}

WWW’21

Mixup for Node and Graph Classification

Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi

In Proceedings of the Web Conference, 2021

@inproceedings{wang2021mixup,
  title = {Mixup for Node and Graph Classification},
  author = {Wang, Yiwei and Wang, Wei and Liang, Yuxuan and Cai, Yujun and Hooi, Bryan},
  booktitle = {Proceedings of the Web Conference},
  pages = {3663--3674},
  year = {2021}
}

WWW’21

Curgraph: Curriculum learning for graph classification

Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi

In Proceedings of the Web Conference, 2021

@inproceedings{wang2021curgraph,
  title = {Curgraph: Curriculum learning for graph classification},
  author = {Wang, Yiwei and Wang, Wei and Liang, Yuxuan and Cai, Yujun and Hooi, Bryan},
  booktitle = {Proceedings of the Web Conference},
  pages = {1238--1248},
  year = {2021}
}

NeurIPS’21

Directed Graph Contrastive Learning

Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, and Changhu Wang

Advances in Neural Information Processing Systems 2021

@article{tong2021directed,
  title = {Directed Graph Contrastive Learning},
  author = {Tong, Zekun and Liang, Yuxuan and Ding, Henghui and Dai, Yongxing and Li, Xinke and Wang, Changhu},
  journal = {Advances in Neural Information Processing Systems},
  volume = {34},
  pages = {19580--19593},
  year = {2021}
}

NeurIPS’21

Adaptive Data Augmentation on Temporal Graphs

Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, and Bryan Hooi

Advances in Neural Information Processing Systems 2021

@article{wang2021adaptive,
  title = {Adaptive Data Augmentation on Temporal Graphs},
  author = {Wang, Yiwei and Cai, Yujun and Liang, Yuxuan and Ding, Henghui and Wang, Changhu and Bhatia, Siddharth and Hooi, Bryan},
  journal = {Advances in Neural Information Processing Systems},
  volume = {34},
  pages = {1440--1452},
  year = {2021}
}

MM’21

Learning Multi-context Aware Location Representations from Large-scale Geotagged Images

Yifang Yin, Ying Zhang, Zhenguang Liu, Yuxuan Liang, Sheng Wang, Rajiv Ratn Shah, and Roger Zimmermann

In Proceedings of the 29th ACM International Conference on Multimedia, 2021

@inproceedings{yin2021learning,
  title = {Learning Multi-context Aware Location Representations from Large-scale Geotagged Images},
  author = {Yin, Yifang and Zhang, Ying and Liu, Zhenguang and Liang, Yuxuan and Wang, Sheng and Shah, Rajiv Ratn and Zimmermann, Roger},
  booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
  pages = {899--907},
  year = {2021}
}

Visual Cascade Analytics of Large-Scale Spatiotemporal Data

Zikun Deng, Di Weng, Yuxuan Liang, Jie Bao, Yu Zheng, Tobias Schreck, Mingliang Xu, and Yingcai Wu

IEEE Transactions on Visualization and Computer Graphics 2021

@article{deng2021visual,
  title = {Visual Cascade Analytics of Large-Scale Spatiotemporal Data},
  author = {Deng, Zikun and Weng, Di and Liang, Yuxuan and Bao, Jie and Zheng, Yu and Schreck, Tobias and Xu, Mingliang and Wu, Yingcai},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  volume = {28},
  number = {6},
  pages = {2486--2499},
  year = {2021},
  publisher = {IEEE}
}

Structure-Aware Label Smoothing for Graph Neural Networks

Yiwei Wang, Yujun Cai, Yuxuan Liang, Wei Wang, Henghui Ding, Muhao Chen, Jing Tang, and Bryan Hooi

arXiv preprint arXiv:2112.00499 2021

@article{wang2021structure,
  title = {Structure-Aware Label Smoothing for Graph Neural Networks},
  author = {Wang, Yiwei and Cai, Yujun and Liang, Yuxuan and Wang, Wei and Ding, Henghui and Chen, Muhao and Tang, Jing and Hooi, Bryan},
  journal = {arXiv preprint arXiv:2112.00499},
  year = {2021}
}

Decoupling Long-and Short-Term Patterns in Spatiotemporal Inference

Junfeng Hu, Yuxuan Liang, Zhencheng Fan, Yifang Yin, Ying Zhang, and Roger Zimmermann

arXiv preprint arXiv:2109.09506 2021

@article{hu2021decoupling,
  title = {Decoupling Long-and Short-Term Patterns in Spatiotemporal Inference},
  author = {Hu, Junfeng and Liang, Yuxuan and Fan, Zhencheng and Yin, Yifang and Zhang, Ying and Zimmermann, Roger},
  journal = {arXiv preprint arXiv:2109.09506},
  year = {2021}
}

2020

KDD’20

Nodeaug: Semi-Supervised Node Classification with Data Augmentation

Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Juncheng Liu, and Bryan Hooi

In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020

Bib PDF Cited by 79

@inproceedings{wang2020nodeaug,
  title = {Nodeaug: Semi-Supervised Node Classification with Data Augmentation},
  author = {Wang, Yiwei and Wang, Wei and Liang, Yuxuan and Cai, Yujun and Liu, Juncheng and Hooi, Bryan},
  booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages = {207--217},
  year = {2020}
}

NeurIPS’20

Digraph Inception Convolutional Networks

Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, and Andrew Lim

Advances in neural information processing systems 2020

Bib PDF Cited by 56

@article{tong2020digraph,
  title = {Digraph Inception Convolutional Networks},
  author = {Tong, Zekun and Liang, Yuxuan and Sun, Changsheng and Li, Xinke and Rosenblum, David and Lim, Andrew},
  journal = {Advances in neural information processing systems},
  volume = {33},
  pages = {17907--17918},
  year = {2020}
}

ECML-PKDD’20

Revisiting convolutional neural networks for citywide crowd flow analytics

Yuxuan Liang, Kun Ouyang, Yiwei Wang, Ye Liu, Junbo Zhang, Yu Zheng, and David S Rosenblum

In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2020

@inproceedings{liang2020revisiting,
  title = {Revisiting convolutional neural networks for citywide crowd flow analytics},
  author = {Liang, Yuxuan and Ouyang, Kun and Wang, Yiwei and Liu, Ye and Zhang, Junbo and Zheng, Yu and Rosenblum, David S},
  booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages = {578--594},
  year = {2020},
  organization = {Springer}
}

KDD’20

Autost: Efficient Neural Architecture Search for Spatio-Temporal Prediction

Ting Li, Junbo Zhang, Kainan Bao, Yuxuan Liang, Yexin Li, and Yu Zheng

In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020

@inproceedings{li2020autost,
  title = {Autost: Efficient Neural Architecture Search for Spatio-Temporal Prediction},
  author = {Li, Ting and Zhang, Junbo and Bao, Kainan and Liang, Yuxuan and Li, Yexin and Zheng, Yu},
  booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages = {794--802},
  year = {2020}
}

Fine-grained Urban Flow Inference

Kun Ouyang, Yuxuan Liang, Ye Liu, Zekun Tong, Sijie Ruan, David Rosenblum, and Yu Zheng

IEEE transactions on knowledge and data engineering 2020

@article{ouyang2020fine,
  title = {Fine-grained Urban Flow Inference},
  author = {Ouyang, Kun and Liang, Yuxuan and Liu, Ye and Tong, Zekun and Ruan, Sijie and Rosenblum, David and Zheng, Yu},
  journal = {IEEE transactions on knowledge and data engineering},
  year = {2020},
  publisher = {IEEE}
}

Predicting Citywide Crowd Flows in Irregular Regions using Multi-View Graph Convolutional Networks

Junkai Sun, Junbo Zhang, Qiaofei Li, Xiuwen Yi, Yuxuan Liang, and Yu Zheng

IEEE Transactions on Knowledge and Data Engineering 2020

Bib PDF Cited by 122

@article{sun2020predicting,
  title = {Predicting Citywide Crowd Flows in Irregular Regions using Multi-View Graph Convolutional Networks},
  author = {Sun, Junkai and Zhang, Junbo and Li, Qiaofei and Yi, Xiuwen and Liang, Yuxuan and Zheng, Yu},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2020},
  publisher = {IEEE}
}

Spatio-Temporal Meta Learning for Urban Traffic Prediction

Zheyi Pan, Wentao Zhang, Yuxuan Liang, Weinan Zhang, Yong Yu, Junbo Zhang, and Yu Zheng

IEEE Transactions on Knowledge and Data Engineering 2020

@article{pan2020spatio,
  title = {Spatio-Temporal Meta Learning for Urban Traffic Prediction},
  author = {Pan, Zheyi and Zhang, Wentao and Liang, Yuxuan and Zhang, Weinan and Yu, Yong and Zhang, Junbo and Zheng, Yu},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2020},
  publisher = {IEEE}
}

Predicting Urban Water Quality with Ubiquitous Data - a Data-Driven Approach

Ye Liu, Yuxuan Liang, Kun Ouyang, Shuming Liu, David Rosenblum, and Yu Zheng

IEEE Transactions on Big Data 2020

@article{liu2020predicting,
  title = {Predicting Urban Water Quality with Ubiquitous Data - a Data-Driven Approach},
  author = {Liu, Ye and Liang, Yuxuan and Ouyang, Kun and Liu, Shuming and Rosenblum, David and Zheng, Yu},
  journal = {IEEE Transactions on Big Data},
  year = {2020},
  publisher = {IEEE}
}

UBICOMP’20

Dynamic Public Resource Allocation based on Human Mobility Prediction

Sijie Ruan, Jie Bao, Yuxuan Liang, Ruiyuan Li, Tianfu He, Chuishi Meng, Yanhua Li, Yingcai Wu, and Yu Zheng

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 2020

@article{ruan2020dynamic,
  title = {Dynamic Public Resource Allocation based on Human Mobility Prediction},
  author = {Ruan, Sijie and Bao, Jie and Liang, Yuxuan and Li, Ruiyuan and He, Tianfu and Meng, Chuishi and Li, Yanhua and Wu, Yingcai and Zheng, Yu},
  journal = {Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies},
  volume = {4},
  number = {1},
  pages = {1--22},
  year = {2020},
  publisher = {ACM New York, NY, USA}
}

AAAI’20

Learning to Generate Maps from Trajectories

Sijie Ruan, Cheng Long, Jie Bao, Chunyang Li, Zisheng Yu, Ruiyuan Li, Yuxuan Liang, Tianfu He, and Yu Zheng

In Proceedings of the AAAI conference on artificial intelligence, 2020

@inproceedings{ruan2020learning,
  author = {Ruan, Sijie and Long, Cheng and Bao, Jie and Li, Chunyang and Yu, Zisheng and Li, Ruiyuan and Liang, Yuxuan and He, Tianfu and Zheng, Yu},
  booktitle = {Proceedings of the AAAI conference on artificial intelligence},
  volume = {34},
  number = {01},
  pages = {890--897},
  year = {2020}
}

ECML-PKDD’20

Progressive Supervision for Node Classification

Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi

In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2020

@inproceedings{wang2020progressive,
  title = {Progressive Supervision for Node Classification},
  author = {Wang, Yiwei and Wang, Wei and Liang, Yuxuan and Cai, Yujun and Hooi, Bryan},
  booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages = {266--281},
  year = {2020},
  organization = {Springer}
}

Federated Forest

Yang Liu, Yingting Liu, Zhijie Liu, Yuxuan Liang, Chuishi Meng, Junbo Zhang, and Yu Zheng

IEEE Transactions on Big Data 2020

Bib PDF Cited by 117

@article{liu2020federated,
  title = {Federated Forest},
  author = {Liu, Yang and Liu, Yingting and Liu, Zhijie and Liang, Yuxuan and Meng, Chuishi and Zhang, Junbo and Zheng, Yu},
  journal = {IEEE Transactions on Big Data},
  year = {2020},
  publisher = {IEEE}
}

IJCNN’20

Unsupervised Learning of Disentangled Location Embeddings

Kun Ouyang, Yuxuan Liang, Ye Liu, David S Rosenblum, and Wenzhuo Yang

In 2020 International Joint Conference on Neural Networks (IJCNN), 2020

@inproceedings{ouyang2020unsupervised,
  title = {Unsupervised Learning of Disentangled Location Embeddings},
  author = {Ouyang, Kun and Liang, Yuxuan and Liu, Ye and Rosenblum, David S and Yang, Wenzhuo},
  booktitle = {2020 International Joint Conference on Neural Networks (IJCNN)},
  pages = {1--8},
  year = {2020},
  organization = {IEEE}
}

Graphcrop: Subgraph cropping for graph classification

Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi

arXiv preprint arXiv:2009.10564 2020

@article{wang2020graphcrop,
  title = {Graphcrop: Subgraph cropping for graph classification},
  author = {Wang, Yiwei and Wang, Wei and Liang, Yuxuan and Cai, Yujun and Hooi, Bryan},
  journal = {arXiv preprint arXiv:2009.10564},
  year = {2020}
}

2019

KDD’19

Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning

Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, and Junbo Zhang

In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019

Bib PDF Code Cited by 332

@inproceedings{pan2019urban,
  title = {Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning},
  author = {Pan, Zheyi and Liang, Yuxuan and Wang, Weifeng and Yu, Yong and Zheng, Yu and Zhang, Junbo},
  booktitle = {Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
  pages = {1720--1730},
  year = {2019}
}

KDD’19

Urbanfm: Inferring Fine-Grained Urban Flows

Yuxuan Liang+, Kun Ouyang+, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S Rosenblum, and Yu Zheng

In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019

Bib PDF Code Cited by 90

@inproceedings{liang2019urbanfm,
  title = {Urbanfm: Inferring Fine-Grained Urban Flows},
  author = {Liang+, Yuxuan and Ouyang+, Kun and Jing, Lin and Ruan, Sijie and Liu, Ye and Zhang, Junbo and Rosenblum, David S and Zheng, Yu},
  booktitle = {Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
  pages = {3132--3142},
  year = {2019}
}

IJCAI’19

Learning Multi-Objective Rewards and User Utility Function in Contextual Bandits for Personalized Ranking.

Nirandika Wanigasekara, Yuxuan Liang, Siong Thye Goh, Ye Liu, Joseph Jay Williams, and David S Rosenblum

In International Joint Conference on Artificial Intelligence, 2019

@inproceedings{wanigasekara2019learning,
  title = {Learning Multi-Objective Rewards and User Utility Function in Contextual Bandits for Personalized Ranking.},
  author = {Wanigasekara, Nirandika and Liang, Yuxuan and Goh, Siong Thye and Liu, Ye and Williams, Joseph Jay and Rosenblum, David S},
  booktitle = {International Joint Conference on Artificial Intelligence},
  pages = {3835--3841},
  year = {2019}
}

2018

IJCAI’18

GeoMAN: Multi-Level Attention Networks for Geo-sensory Time Series Prediction.

Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, and Yu Zheng

In International Joint Conference on Artificial Intelligence, 2018

Bib PDF Code Cited by 408

@inproceedings{liang2018geoman,
  title = {GeoMAN: Multi-Level Attention Networks for Geo-sensory Time Series Prediction.},
  author = {Liang, Yuxuan and Ke, Songyu and Zhang, Junbo and Yi, Xiuwen and Zheng, Yu},
  booktitle = {International Joint Conference on Artificial Intelligence},
  volume = {2018},
  pages = {3428--3434},
  year = {2018}
}

2017

SIGSPATIAL’17

Inferring Traffic Cascading Patterns

Yuxuan Liang, Zhongyuan Jiang, and Yu Zheng

In Proceedings of the 25th acm sigspatial international conference on advances in geographic information systems, 2017

@inproceedings{liang2017inferring,
  title = {Inferring Traffic Cascading Patterns},
  author = {Liang, Yuxuan and Jiang, Zhongyuan and Zheng, Yu},
  booktitle = {Proceedings of the 25th acm sigspatial international conference on advances in geographic information systems},
  pages = {1--10},
  year = {2017}
}

2016

IJCAI’16

Urban Water Quality Prediction based on Multi-Task Multi-View Learning

Ye Liu, Yu Zheng, Yuxuan Liang, Shuming Liu, and David S Rosenblum

In Proceedings of the 25th international joint conference on artificial intelligence, 2016

Bib PDF Code Cited by 212

@inproceedings{liu2016urban,
  title = {Urban Water Quality Prediction based on Multi-Task Multi-View Learning},
  author = {Liu, Ye and Zheng, Yu and Liang, Yuxuan and Liu, Shuming and Rosenblum, David S},
  booktitle = {Proceedings of the 25th international joint conference on artificial intelligence},
  year = {2016}
}