28 citations · 150 across the 50 of their papers we have counts for
7 papers · 1 filter
Three Pillars Towards Next-Generation Routing System
Lei Li, Mengxuan Zhang, Zizhuo Xu +2
The routing results are playing an increasingly important role in transportation efficiency, but they could generate traffic congestion unintentionally. This is because the traffic…
High Throughput Shortest Distance Query Processing on Large Dynamic Road Networks
Xinjie Zhou, Mengxuan Zhang, Lei Li +1
Shortest path (SP) computation is the building block for many location-based services, and achieving high throughput SP query processing with real-time response is crucial for thos…
Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models
Jialiang Wang, Hanmo Liu, Shimin Di +4
High-level automation is increasingly critical in AI, driven by rapid advances in large language models (LLMs) and AI agents. However, LLMs, despite their general reasoning power,…
Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline
Shuhao Li, Yue Cui, Jingyi Xu +5
Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years. With the advan…
Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era
Wei Chen, Yuanshao Zhu, Yanchuan Chang +11
Trajectory computing is a pivotal domain encompassing trajectory data management and mining, garnering widespread attention due to its crucial role in various practical application…
Adaptive Hypergraph Network for Trust Prediction
Rongwei Xu, Guanfeng Liu, Yan Wang +3
Trust plays an essential role in an individual's decision-making. Traditional trust prediction models rely on pairwise correlations to infer potential relationships between users.…
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
Pengfei Ding, Yan Wang, Guanfeng Liu +2
Heterogeneous graph few-shot learning (HGFL) has been developed to address the label sparsity issue in heterogeneous graphs (HGs), which consist of various types of nodes and edges…