An Extended Kalman Filter Integrated Latent Feature Model on Dynamic Weighted Directed Graphs

TL;DR

This paper introduces a novel EKLF model that combines the Extended Kalman Filter with latent feature learning to improve the accuracy and efficiency of modeling dynamic weighted directed graphs, especially under strong fluctuations.

Contribution

It proposes a model-driven approach integrating EKF and ALS algorithms for better representation and prediction of DWDGs, outperforming existing data-driven methods.

Findings

Outperforms state-of-the-art models in prediction accuracy.

Achieves higher computational efficiency.

Effectively captures complex temporal patterns in DWDGs.

Abstract

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.