Publications

Preprints

• DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition [Code]
  Yuxuan Liang, Pan Zhou, Roger Zimmermann and Shuicheng Yan
  Arxiv preprint, 2021.

• Spatio-Temporal Graph Contrastive Learning
  Xu Liu*, Yuxuan Liang*, Yu Zheng, Bryan Hooi and Roger Zimmermann (* indicates equal contribution)
  Arxiv preprint, 2021.

• PRNet: A Periodic Residual Learning Network for Crowd Flow Forecasting
  Chengxin Wang, Yuxuan Liang and Gary Tan
  Arxiv preprint, 2021.

• 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, 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, 2021.

• Graphcrop: Subgraph cropping for graph classification
  Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai and Bryan Hooi
  Arxiv preprint, 2020

Peer-reviewed Papers

Spatio-Temporal Data Mining

• Modeling Trajectories with Neural Ordinary Differential Equations
  Yuxuan Liang, Kun Ouyang, Hanshu Yan, Yiwei Wang, Zekun Tong and Roger Zimmermann
  in Proc. of The 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021.

• Fine-Grained Urban Flow Prediction
  Yuxuan Liang, Kun Ouyang, Junkai Sun, Yiwei Wang, Junbo Zhang, Yu Zheng, David Rosenblum and Roger Zimmermann
  The Web Conference 2021 (WWW), 2021.

• Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics [Code]
  Yuxuan Liang, Kun Ouyang, Yiwei Wang, Ye Liu, Junbo Zhang, Yu Zheng, David S. Rosenblum
  in Proc. of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2020.

• UrbanFM: Inferring Fine-Grained Urban Flows [Code]
  Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S. Rosenblum, Yu Zheng
  in Proc. of The ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019.

• GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction [Code]
  Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, Yu Zheng
  in Proc. of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018.

• Inferring Traffic Cascading Patterns
  Yuxuan Liang, Zhongyuan Jiang, Yu Zheng
  in Proc. of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS), 2017.

• 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 (IEEE TVCG), 2021.

• 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
  ACM Multimedia (MM), 2021.

• AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph [Code]
  Zheyi Pan, Songyu Ke, Xiaodu Yang, Yuxuan Liang, Yong Yu, Junbo Zhang, Yu Zheng
  The Web Conference 2021 (WWW), 2021.

• AutoST: Efficient Neural Architecture Search forSpatio-Temporal Prediction
  Ting Li, Junbo Zhang, Kainan Bao, Yuxuan Liang, Yexin Li, Yu Zheng
  in Proc. of The ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.

• Fine-Grained Urban Flow Inference [Code]
  Kun Ouyang, Yuxuan Liang, Ye Liu, Zekun Tong, Sijie Ruan, Yu Zheng, David S. Rosenblum
  in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

• Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks
  Junkai Sun, Junbo Zhang, Qiaofei Li, Xiuwen Yi, Yuxuan Liang, Yu Zheng
  in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

• Spatio-Temporal Meta Learning for Urban Traffic Prediction [Code]
  Zheyi Pan, Wentao Zhang, Yuxuan Liang, Weinan Zhang, Yong Yu, Junbo Zhang, Yu Zheng
  in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

• Predicting Urban Water Quality with Ubiquitous Data - A Data-driven Approach [Code]
  Ye Liu, Yuxuan Liang, Kun Ouyang, Shuming Liu, David S. Rosenblum, Yu Zheng
  in IEEE Transactions on Big Data (TBD), 2020.

• Unsupervised Learning of Disentangled Location Embeddings
  Kun Ouyang, Yuxuan Liang, Wenzhuo Yang, Ye Liu, David S. Rosenblum
  in Proc. of The International Joint Conference on Neural Networks (IJCNN), 2020.

• 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, Yu Zheng
  in Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp), 2020.

• Learning to Generate Maps from Trajectories
  Sijie Ruan, Cheng Long, Jie Bao, Chunyang Li, Zisheng Yu, Ruiyuan Li, Yuxuan Liang, Tianfu He, Yu Zheng
  in Proc. of the 34th AAAI Conference, 2020.

• Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning [Code]
  Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang
  in Proc. of The ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019.

• Urban Water Quality Prediction based on Multi-task Multi-view Learning [Code]
  Ye Liu, Yu Zheng, Yuxuan Liang, Shuming Liu, David S. Rosenblum
  in Proc. of the 25th International Joint Conference on Artificial Intelligence (IJCAI), 2016.

Graph Neural Networks

• Directed Graph Contrastive Learning
  Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li and Changhu Wang
  in Proc. of The Advances in Neural Information Processing Systems (NeurIPS), 2021.

• Adaptive data augmentation on temporal graphs
  Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia and Bryan Hooi
  in Proc. of The Advances in Neural Information Processing Systems (NeurIPS), 2021.

• Mixup for Node and Graph Classification [Code]
  Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai and Bryan Hooi
  The Web Conference 2021 (WWW), 2021.

• CurGraph: Curriculum Learning for Graph Classification
  Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai and Bryan Hooi
  The Web Conference 2021 (WWW), 2021.

• Digraph Inception Convolutional Networks [Code]
  Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David S. Rosenblum, Andrew Lim
  in Proc. of The Advances in Neural Information Processing Systems (NeurIPS), 2020.

• Progressive Supervision for Node Classification
  Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Bryan Hooi
  in Proc. of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2020.

• NodeAug: Semi-Supervised Node Classification with Data Augmentation
  Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Juncheng Liu, Bryan Hooi
  in Proc. of The ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.

Other Topics

• Federated Forest
  Yang Liu, Yingting Liu, Zhijie Liu, Yuxuan Liang, Chuishi Meng, Junbo Zhang, Yu Zheng
  in IEEE Transactions on Big Data (TBD), 2020.

• Learning Multi-Objective Rewards and User Utility Function in Contextual Bandits for Personalized Ranking
  Nirandika Wanigasekara, Yuxuan Liang, Siong Thye Goh, Ye Liu, Joseph J. Williams, David S. Rosenblum
  in Proc. of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019