An Extended Kalman Filter Integrated Latent Feature Model on Dynamic Weighted Directed Graphs
<|reference_start|>An Extended Kalman Filter Integrated Latent Feature Model on Dynamic Weighted Directed Graphs: A dynamic weighted directed graph (DWDG) is commonly encountered in various application scenarios. It involves extensive dynamic interactions among numerous nodes. Most existing approaches explore the intricate temporal patterns hidden in a DWDG from the purely data-driven perspective, which suffers from accuracy loss when a DWDG exhibits strong fluctuations over time. To address this issue, this study proposes a novel Extended-Kalman-Filter-Incorporated Latent Feature (EKLF) model to represent a DWDG from the model-driven perspective. Its main idea is divided into the following two-fold ideas: a) adopting a control model, i.e., the Extended Kalman Filter (EKF), to track the complex temporal patterns precisely with its nonlinear state-transition and observation functions; and b) introducing an alternating least squares (ALS) algorithm to train the latent features (LFs) alternatively for precisely representing a DWDG. Empirical studies on DWDG datasets demonstrate that the proposed EKLF model outperforms state-of-the-art models in prediction accuracy and computational efficiency for missing edge weights of a DWDG. It unveils the potential for precisely representing a DWDG by incorporating a control model.<|reference_end|>
@article{zhou2024an,
    title={An Extended Kalman Filter Integrated Latent Feature Model on Dynamic
  Weighted Directed Graphs},
    author={Hongxun Zhou, Xiangyu Chen, and Ye Yuan},
    journal={arXiv preprint arXiv:2407.21376},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21376},
    primaryClass={cs.AI}
}