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NetSim -- The framework for complex network generator
Social and Information Networks · Submitted 26 May 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Networks are everywhere and their many types, including social networks, the Internet, food webs etc., have been studied for the last few decades. | background 1.00 | 0.03 | 0.01 | asserted 1.00 |
| s1 | 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. | gap 0.73 | 0.08 | 0.02 | asserted 1.00 |
| s2 | 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. | contribution 1.00 | 0.82 | 0.07 | asserted 0.97 |
| s3 | Our approach enables to simulate three classical network models (random, small-world and scale-free) with easily adjustable model parameters and network size. | method 0.78 | 0.81 | 0.05 | asserted 1.00 |
| s4 | To be able to compare different networks, for a single experimental setup we kept the number of edges and vertices fixed across the models. | method 0.99 | 0.94 | 0.03 | asserted 0.99 |
| s5 | 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. | method 0.98 | 0.97 | 0.06 | asserted 0.99 |
| s6 | 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. | result 1.00 | 0.94 | 0.78 | asserted 0.99 |
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The paper
Original 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.
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