From Cubes to Networks: Fast Generic Model for Synthetic Networks Generation

TL;DR

This paper introduces FGM, a fast and generic model that transforms multi-dimensional cube data into realistic synthetic networks, capturing key network properties efficiently and resiliently.

Contribution

FGM is the first model to convert cubes into networks using nearest-neighbor search, producing more authentic and consistent synthetic networks compared to previous methods.

Findings

FGM generates networks with realistic degree distributions.

Networks exhibit power-law nearest-neighbor degree dependency.

FGM is resilient to input perturbations, maintaining network properties.

Abstract

Analytical explorations on complex networks and cubes (i.e., multi-dimensional datasets) are currently two separate research fields with different strategies. To gain more insights into cube dynamics via unique network-domain methodologies and to obtain abundant synthetic networks, we need a transformation approach from cubes into associated networks. To this end, we propose FGM, a fast generic model converting cubes into interrelated networks, whereby samples are remodeled into nodes and network dynamics are guided under the concept of nearest-neighbor searching. Through comparison with previous models, we show that FGM can cost-efficiently generate networks exhibiting typical patterns more closely aligned to factual networks, such as more authentic degree distribution, power-law average nearest-neighbor degree dependency, and the influence decay phenomenon we consider vital for networks. Furthermore, we evaluate the networks that FGM generates through various cubes. Results show that FGM is resilient to input perturbations, producing networks with consistent fine properties.