From Cubes to Networks: Fast Generic Model for Synthetic Networks Generation
This work addresses the need for synthetic network generation from cubes to bridge analytical gaps between network and cube research, though it appears incremental as it builds on existing transformation approaches.
The paper tackles the problem of transforming multi-dimensional datasets (cubes) into synthetic networks to enable network-domain analyses, proposing FGM, a fast generic model that generates networks with more authentic patterns like degree distribution and power-law dependencies compared to previous models, showing cost-efficiency and resilience to input perturbations.
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.