How the use of feature selection methods influences the efficiency and accuracy of complex network simulations

TL;DR

This study investigates how feature selection methods impact the accuracy and efficiency of complex network simulations, proposing a new method that improves simulation results by selecting optimal node features.

Contribution

It introduces FS-SNS, a feature selection approach combining unsupervised filtering and wrapper techniques, enhancing simulation accuracy in complex networks.

Findings

FS-SNS improved 8 out of 10 network simulations

A threshold of 4 features yielded optimal accuracy

The method advances real-world network modeling

Abstract

Complex network systems' models are designed to perfectly emulate real-world networks through the use of simulation and link prediction. Complex network systems are defined by nodes and their connections where both have real-world features that result in a heterogeneous network in which each of the nodes has distinct characteristics. Thus, incorporating real-world features is an important component to achieve a simulation which best represents the real-world. Currently very few complex network systems implement real-world features, thus this study proposes feature selection methods which utilise unsupervised filtering techniques to rank real-world node features alongside a wrapper function to test combinations of the ranked features. The chosen method was coined FS-SNS which improved 8 out of 10 simulations of real-world networks. A consistent threshold of included features was also discovered which saw a threshold of 4 features to achieve the most accurate simulation for all networks. Through these findings the study also proposes future work and discusses how the findings can be used to further the Digital Twin and complex network system field.