NetSim -- The framework for complex network generator
Abstract
Networks are everywhere and their many types, including social networks, the Internet, food webs etc., have been studied for the last few decades. However, in real-world networks, it's hard to find examples that can be easily comparable, i.e. have the same density or even number of nodes and edges. We propose a flexible and extensible NetSim framework to understand how properties in different types of networks change with varying number of edges and vertices. Our approach enables to simulate three classical network models (random, small-world and scale-free) with easily adjustable model parameters and network size. To be able to compare different networks, for a single experimental setup we kept the number of edges and vertices fixed across the models. To understand how they change depending on the number of nodes and edges we ran over 30,000 simulations and analysed different network characteristics that cannot be derived analytically. Two of the main findings from the analysis are that the average shortest path does not change with the density of the scale-free network but changes for small-world and random networks; the apparent difference in mean betweenness centrality of the scale-free network compared with random and small-world networks.
Cite
@article{arxiv.1805.10520,
title = {NetSim -- The framework for complex network generator},
author = {Akanda Wahid -Ul- Ashraf and Marcin Budka and Katarzyna Musial},
journal= {arXiv preprint arXiv:1805.10520},
year = {2018}
}
Comments
This paper has been accepted for the 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2018). The conference will be held in September, 2018. The original paper has 10 pages but the arXiv version has 11 pages due to slightly bigger fonts