We live in a connected world, where networks dominate our economy, our environment and society, and
most people are not aware of them. Understanding these networks can aid researchers in prevent devastating
outcomes. While real networks are insightful, they are usually hard to obtain (especially samples of the same
type of network), they have PII information and many times they are at the wrong scale.

Thus, network scientists desire methodologies to create synthetic networks that resemble the real net-
works to get insights from the real world networks. Additionally, synthetic networks proves the researchers
modalities to change some parameters while keeping others constant to create different scale of given networks by preserving certain properties observed in the real networks. Generative models aim to explain how networks form and evolve specific structural features.

The goal of this project is to create networks that:
1. have varying parameters to get different scales
2. have similar properties to real ones
3. optional you could bring a focus on multilayer networks, in which you can capture the layer level
topology (matching the properties of the real one when the synthetic network is at the same scale as the real network).

If you work with multilayered network, keep in mind that edges appear in different layers based on the type of relationship they capture. Additionally, if the nodes are identical in each layer, we call this network a multiplex. 



A copy of the pulblication from this group can be found here: Building a Reliable, Dynamic and Temporal Synthetic Model of the World TradeWeb