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 ExtrapolationIn Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
- KDD’23[New] Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal LearningIn Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
- [New] Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A SurveyarXiv preprint arXiv:2303.14483 2023
- [New] AutoSTG+: An Automatic Framework to Discover The Optimal Network for Spatio-temporal Graph PredictionArtificial Intelligence 2023
2022
- Content-Attribute Disentanglement for Generalized Zero-Shot LearningIEEE Access 2022
2021
- WWW’21
2020
- KDD’20Nodeaug: Semi-Supervised Node Classification with Data AugmentationIn Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
- NeurIPS’20Digraph Inception Convolutional NetworksAdvances in neural information processing systems 2020
- Predicting Citywide Crowd Flows in Irregular Regions using Multi-View Graph Convolutional NetworksIEEE Transactions on Knowledge and Data Engineering 2020
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2019
- KDD’19Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta LearningIn Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019
- KDD’19Urbanfm: Inferring Fine-Grained Urban FlowsIn Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019
2018
- IJCAI’18GeoMAN: Multi-Level Attention Networks for Geo-sensory Time Series Prediction.In International Joint Conference on Artificial Intelligence, 2018
2017
2016
- IJCAI’16Urban Water Quality Prediction based on Multi-Task Multi-View LearningIn Proceedings of the 25th international joint conference on artificial intelligence, 2016