An Extended Kalman Filter Integrated Latent Feature Model on Dynamic Weighted Directed Graphs
This work addresses the challenge of modeling dynamic graphs with fluctuations, which is important for applications like network analysis, but it appears incremental as it combines existing methods (EKF and ALS) in a novel way.
The study tackled the problem of accurately representing dynamic weighted directed graphs (DWDGs) with strong temporal fluctuations by proposing an Extended-Kalman-Filter-Incorporated Latent Feature (EKLF) model, which outperformed state-of-the-art models in prediction accuracy and computational efficiency for missing edge weights.
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.