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*5, WWW*4, TKDE*4), AI venues (e.g., NeurIPS*3, IJCAI*4, AAAI*2, ICLR*1), and CV venues (e.g., ECCV*1, MM*2). Here are some representative papers:

2023

  1. ICLR’23
    [New] 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
  2. AAAI’23
    [New] 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

2022

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. Content-Attribute Disentanglement for Generalized Zero-Shot Learning
    Yoojin An, Sangyeon Kim, Yuxuan Liang, Roger Zimmermann, Dongho Kim, and Jihie Kim
    IEEE Access 2022
  9. 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
  10. 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

2021

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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

2020

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. Federated Forest
    Yang Liu, Yingting Liu, Zhijie Liu, Yuxuan Liang, Chuishi Meng, Junbo Zhang, and Yu Zheng
    IEEE Transactions on Big Data 2020
  13. 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
  14. Graphcrop: Subgraph cropping for graph classification
    Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi
    arXiv preprint arXiv:2009.10564 2020

2019

  1. 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
  2. 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
  3. 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

2018

  1. 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

2017

  1. 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

2016

  1. 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