Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning
<|reference_start|>Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning: In the landscape of spatio-temporal data analytics, effective trajectory representation learning is paramount. To bridge the gap of learning accurate representations with efficient and flexible mechanisms, we introduce Efflex, a comprehensive pipeline for transformative graph modeling and representation learning of the large-volume spatio-temporal trajectories. Efflex pioneers the incorporation of a multi-scale k-nearest neighbors (KNN) algorithm with feature fusion for graph construction, marking a leap in dimensionality reduction techniques by preserving essential data features. Moreover, the groundbreaking graph construction mechanism and the high-performance lightweight GCN increase embedding extraction speed by up to 36 times faster. We further offer Efflex in two versions, Efflex-L for scenarios demanding high accuracy, and Efflex-B for environments requiring swift data processing. Comprehensive experimentation with the Porto and Geolife datasets validates our approach, positioning Efflex as the state-of-the-art in the domain. Such enhancements in speed and accuracy highlight the versatility of Efflex, underscoring its wide-ranging potential for deployment in time-sensitive and computationally constrained applications.<|reference_end|>
@article{cheng2024efflex:,
    title={Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory
  Graph Modeling and Representation Learning},
    author={Ming Cheng, Ziyi Zhou, Bowen Zhang, Ziyu Wang, Jiaqi Gan, Ziang Ren,
  Weiqi Feng, Yi Lyu, Hefan Zhang, Xingjian Diao},
    journal={arXiv preprint arXiv:2404.12400},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12400},
    primaryClass={cs.LG}
}