Netsim - The framework for complex network generator
Authors: Wahid-Ul-Ashraf, A., Budka, M. and Musial, K.
Journal: Procedia Computer Science
Volume: 126
Pages: 547-556
eISSN: 1877-0509
DOI: 10.1016/j.procS.2018.07.289
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.
https://eprints.bournemouth.ac.uk/30818/
Source: Scopus
NetSim - The framework for complex network generator
Authors: Wahid-Ul-Ashraf, A., Budka, M. and Musial, K.
Journal: KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (KES-2018)
Volume: 126
Pages: 547-556
ISSN: 1877-0509
DOI: 10.1016/j.procs.2018.07.289
https://eprints.bournemouth.ac.uk/30818/
Source: Web of Science (Lite)
NetSim - The framework for complex network generator.
Authors: Wahid-Ul-Ashraf, A., Budka, M. and Musial, K.
Journal: CoRR
Volume: abs/1805.10520
https://eprints.bournemouth.ac.uk/30818/
Source: DBLP
NetSim -- The framework for complex network generator
Authors: Wahid-Ul-Ashraf, A., Budka, M. and Musial, K.
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.
https://eprints.bournemouth.ac.uk/30818/
Source: arXiv
NetSim: The framework for complex network generator
Authors: Wahid-Ul-Ashraf, A., Budka, M. and Musial, K.
Pages: 547-556
Publisher: Procedia Computer Science 126
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.
https://eprints.bournemouth.ac.uk/30818/
http://kes2018.kesinternational.org/
Source: BURO EPrints