During the last decade the evolution of the research on computer epidemiology has pointed out the use of different graph topologies between older and recent simulation-based studies. The problem that rises up is the analytical comparison of their results which can reveal a lot of valuable information. NGCE as a tool can prove helpful for these comparisons because of its characteristic of reconstructing similar or identical graph topologies and presenting in graphs their statistical properties.

By using the NGCE we reconstructed various graph types, used in the experiments of the most cited papers in the area of computer epidemiology and in this page we present their oucomes. The results that can be found in this page where derived from the graph types of the below mentioned papers:

J. Kephart from the paper "How Topology affects population dynamics" (1992)
		Homogeneous Graph with 10,000 nodes Random Graph with 10,000 nodes and 2 neighbours for each node Random Graph with 10,000 nodes and 8 neighbours for each node
J.Leveille from the paper "Epidemic spreading in technological networks " (2002)
		Homogeneous Graph with 10,000 nodes Scale-Free Graph with 6,250 nodes Scale-free Graph with 10,000 nodes
R. Pastor-Satorras and A. Vespigianni from the paper "Epidemic spreading in scale-free networks" (2001)
		Scale-free Graph with 10,000 nodes
C.Wang, J.Knight and M. Elder from the paper "On computer viral infection and the effect of immunization " (2000)
		Scale-free Graph with 1,000 nodes
J. Kephart from the paper "How Topology affects population dynamics" (1992)
In this specific paper Kephart used the below mentioned types of graphs to study computer epidemiology:
	Homogeneous Graph with 10,000 nodes
	The config text file we used to built this type of graph is: ../ 10000 h
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	Random Graph with 10,000 nodes and 2 neighbours for each node
	The config text file we used to built this type of graph is as follows: ../ 10000 e 0.0001 153 The output of the NGCE and the plot of the graph are listed below:
	Connectivity Distribution Function Plot Structure Plot
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	Random Graph with 10,000 nodes and 8 neighbours for each node
	The config text file we used to built this type of graph is as follows: ../ 10000 e 0.0004 153 The output of the NGCE and the plot of the graph are listed below:
	Connectivity Distribution Function Plot Structure Plot
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J.Leveille from the paper "Epidemic spreading in technological networks " (2002)
In this specific paper Leveille used the below mentioned types of graphs to study computer epidemiology:
	Homogeneous Graph with 10,000 nodes
	The config text file we used to built this type of graph is: ../ 10000 h
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	Scale-Free Graph with 6,250 nodes
	The config text file we used to built this type of graph is: ../ 6250 a 3 2 153 The output of the NGCE and the plot of the graph* are listed below:
	Connectivity Distribution Function Plot Structure Plot
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	Scale-free Graph with 10,000 nodes
	The config text file we used to built this type of graph is: ../ 10000 a 3 2 153 The output of the NGCE and the plot of the graph* are listed below:
	Connectivity Distribution Function Plot Structure Plot
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R. Pastor-Satorras and A. Vespigianni from the paper "Epidemic spreading in scale-free networks" (2001)
In this specific paper they used the below mentioned type of graph to their study:
	Scale-Free Graph with 10,000 nodes
	The config text file we used to built this type of graph is: ../ 10000 a 3 2 153 The output of the NGCE and the plot of the graph* are listed below:
	Connectivity Distribution Function Plot Structure Plot
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C.Wang, J.Knight and M. Elder from the paper "On computer viral infection and the effect of immunization " (2000)
In this specific paper they used a cluster topology model, which is equivalent to a scale-free with 1000 nodes:
	Scale-Free Graph with 1,000 nodes
	The config text file we used to built this type of graph is: ../ 1000 a 3 2 153 The output of the NGCE and the plot of the graph* are listed below:
	Connectivity Distribution Function Plot Structure Plot
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*The plots of the graph topology were made with the Pajek tool.