  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.
