Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleMay 2024
Towards better dynamic graph learning: new architecture and unified library
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing SystemsDecember 2023, Article No.: 2960, Pages 67686–67700We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop interactions by: (i) a neighbor co-occurrence encoding scheme that explores ...
- research-articleDecember 2023
Generic Dynamic Graph Convolutional Network for traffic flow forecasting
AbstractIn the field of traffic forecasting, methods based on Graph Convolutional Network (GCN) are emerging. But existing methods still have limitations due to insufficient sharing patterns, inflexible temporal relations and static relation assumptions. ...
Highlights- A generic and dynamic graph convolutional network named GDGCN is proposed.
- It is the first to explore the parameter-sharing mechanism in traffic forecasting.
- A novel temporal graph convolutional block is designed.
- A dynamic ...
- research-articleOctober 2023
Combinatorial Optimization Meets Reinforcement Learning: Effective Taxi Order Dispatching at Large-Scale
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 10Oct. 2023, Pages 9812–9823https://doi.org/10.1109/TKDE.2021.3127077Ride hailing has become prevailing. Central in ride hailing platforms is taxi order dispatching which involves recommending a suitable driver for each order. Previous works use pure combinatorial optimization solutions for taxi dispatching, which suffer ...
- research-articleAugust 2023
Continuous-Time User Preference Modelling for Temporal Sets Prediction
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 36, Issue 4April 2024, Pages 1475–1488https://doi.org/10.1109/TKDE.2023.3309982Given a sequence of sets, where each set has a timestamp and contains an arbitrary number of elements, temporal sets prediction aims to predict the elements in the subsequent set. Previous studies for temporal sets prediction mainly focus on the modelling ...
- research-articleJune 2023
Heterogeneous Graph Representation Learning With Relation Awareness
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 6June 2023, Pages 5935–5947https://doi.org/10.1109/TKDE.2022.3160208Representation learning on heterogeneous graphs aims to obtain meaningful node representations to facilitate various downstream tasks, such as node classification and link prediction. Existing heterogeneous graph learning methods are primarily developed ...
-
- ArticleApril 2023
Approximate k-Nearest Neighbor Query over Spatial Data Federation
- Kaining Zhang,
- Yongxin Tong,
- Yexuan Shi,
- Yuxiang Zeng,
- Yi Xu,
- Lei Chen,
- Zimu Zhou,
- Ke Xu,
- Weifeng Lv,
- Zhiming Zheng
Database Systems for Advanced ApplicationsApr 2023, Pages 351–368https://doi.org/10.1007/978-3-031-30637-2_23AbstractApproximate nearest neighbor query is a fundamental spatial query widely applied in many real-world applications. In the big data era, there is an increasing demand to scale these queries over a spatial data federation, which consists of multiple ...
- research-articleFebruary 2023
Predicting temporal sets with simplified fully connected networks
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial IntelligenceFebruary 2023, Article No.: 540, Pages 4835–4844https://doi.org/10.1609/aaai.v37i4.25609Given a sequence of sets, where each set contains an arbitrary number of elements, temporal sets prediction aims to predict which elements will appear in the subsequent set. Existing methods for temporal sets prediction are developed on sophisticated ...
- research-articleFebruary 2023
Conditional diffusion based on discrete graph structures for molecular graph generation
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial IntelligenceFebruary 2023, Article No.: 480, Pages 4302–4311https://doi.org/10.1609/aaai.v37i4.25549Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs ...
- research-articleDecember 2022
Label-Enhanced Graph Neural Network for Semi-Supervised Node Classification
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 11Nov. 2023, Pages 11529–11540https://doi.org/10.1109/TKDE.2022.3231660Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classical GNNs solely use the known ...
- letterDecember 2022
Revenue-maximizing online stable task assignment on taxi-dispatching platforms
Frontiers of Computer Science: Selected Publications from Chinese Universities (FCS), Volume 16, Issue 6Dec 2022https://doi.org/10.1007/s11704-021-0363-3ConclusionWe identify a problem about dynamic task assignment, called RMOSM Problem, then introduce the concept Substitutable and design a novel algorithm Equation-Substitutable Online Matching (ESOM). Finally we conduct experiments that verify the ...
- research-articleOctober 2022
Structure entropy minimization-based dynamic social interaction modeling for trajectory prediction
Information Sciences: an International Journal (ISCI), Volume 614, Issue COct 2022, Pages 170–184https://doi.org/10.1016/j.ins.2022.10.024Highlights- A novel dynamic social interaction modeling mechanism based on structure entropy minimization.
Trajectory is an important basis for reflecting the behavior of moving agents and can be used for various applications. Autonomous systems navigating in complex scenes should have the ability to predict the future locations of ...
- research-articleAugust 2022
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningAugust 2022, Pages 516–524https://doi.org/10.1145/3534678.3539273Traffic demand forecasting by deep neural networks has attracted widespread interest in both academia and industry society. Among them, the pairwise Origin-Destination (OD) demand prediction is a valuable but challenging problem due to several factors: (...
- research-articleAugust 2022
Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningAugust 2022, Pages 4079–4089https://doi.org/10.1145/3534678.3539047Learning based order dispatching has witnessed tremendous success in ride hailing. However, the success halts within individual ride hailing platforms because sharing raw order dispatching data across platforms may leak user privacy and business secrets. ...
Hu-fu: a data federation system for secure spatial queries
- Xuchen Pan,
- Yongxin Tong,
- Chunbo Xue,
- Zimu Zhou,
- Junping Du,
- Yuxiang Zeng,
- Yexuan Shi,
- Xiaofei Zhang,
- Lei Chen,
- Yi Xu,
- Ke Xu,
- Weifeng Lv
Proceedings of the VLDB Endowment (PVLDB), Volume 15, Issue 12Pages 3582–3585https://doi.org/10.14778/3554821.3554849The increasing concerns on data security limit the sharing of data distributedly stored at multiple data owners and impede the scale of spatial queries over big urban data. In response, data federation systems have emerged to perform secure queries ...
- research-articleMay 2022
Harnessing Context for Budget-Limited Crowdsensing With Massive Uncertain Workers
IEEE/ACM Transactions on Networking (TON), Volume 30, Issue 5Pages 2231–2245https://doi.org/10.1109/TNET.2022.3169180Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a crowd of workers are recruited to perform sensing tasks collaboratively. Although it has stimulated many applications, an open fundamental problem is how to select among a massive ...
- research-articleApril 2022
Element-guided Temporal Graph Representation Learning for Temporal Sets Prediction
WWW '22: Proceedings of the ACM Web Conference 2022April 2022, Pages 1902–1913https://doi.org/10.1145/3485447.3512064Given a sequence of sets with timestamps, where each set includes an arbitrary number of elements, temporal sets prediction aims to predict elements in the consecutive set. Indeed, predicting temporal sets is much more complicated than the conventional ...
- research-articleMarch 2022
Graph Sequence Neural Network with an Attention Mechanism for Traffic Speed Prediction
ACM Transactions on Intelligent Systems and Technology (TIST), Volume 13, Issue 2Article No.: 20, Pages 1–24https://doi.org/10.1145/3470889Recent years have witnessed the emerging success of Graph Neural Networks (GNNs) for modeling graphical data. A GNN can model the spatial dependencies of nodes in a graph based on message passing through node aggregation. However, in many application ...
Hu-Fu: efficient and secure spatial queries over data federation
- Yongxin Tong,
- Xuchen Pan,
- Yuxiang Zeng,
- Yexuan Shi,
- Chunbo Xue,
- Zimu Zhou,
- Xiaofei Zhang,
- Lei Chen,
- Yi Xu,
- Ke Xu,
- Weifeng Lv
Proceedings of the VLDB Endowment (PVLDB), Volume 15, Issue 6Pages 1159–1172https://doi.org/10.14778/3514061.3514064Data isolation has become an obstacle to scale up query processing over big data, since sharing raw data among data owners is often prohibitive due to security concerns. A promising solution is to perform secure queries over a federation of multiple data ...
- research-articleAugust 2021
Representation Learning on Knowledge Graphs for Node Importance Estimation
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningAugust 2021, Pages 646–655https://doi.org/10.1145/3447548.3467342In knowledge graphs, there are usually different types of nodes, multiple heterogeneous relations, and numerous attributes of nodes and edges, which impose the challenges on the task of Node Importance Estimation (NIE). Indeed, existing NIE approaches, ...
- research-articleAugust 2021
Learning to Assign: Towards Fair Task Assignment in Large-Scale Ride Hailing
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningAugust 2021, Pages 3549–3557https://doi.org/10.1145/3447548.3467085Ride hailing is a widespread shared mobility application where the central issue is to assign taxi requests to drivers with various objectives. Despite extensive research on task assignment in ride hailing, the fairness of earnings among drivers is ...