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

arXiv: https://arxiv.org/abs/2211.02811 · HTML: https://mlsanity.com/paper/2211.02811/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [Multi-scale Attributed Node Embedding](https://mlsanity.com/paper/1909.13021/) (2019)
- [NetGAN: Generating Graphs via Random Walks](https://mlsanity.com/paper/1803.00816/) (2018)
- [Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models](https://mlsanity.com/paper/2005.07959/) (2020)
- [Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings](https://mlsanity.com/paper/2101.03091/) (2021)
- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)
- [Efficient Network Generation Under General Preferential Attachment](https://mlsanity.com/paper/1403.4521/) (2014)
- [Signed Network Modeling Based on Structural Balance Theory](https://mlsanity.com/paper/1710.09485/) (2017)
- [Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks](https://mlsanity.com/paper/1807.03113/) (2018)
- [Joint Estimation of the Non-parametric Transitivity and Preferential Attachment Functions in Scientific Co-authorship Networks](https://mlsanity.com/paper/1910.00213/) (2019)
- [An Agent-Based Modelling Approach to Brain Drain](https://mlsanity.com/paper/2103.03234/) (2021)

## Abstract (arXiv)

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.