While designing future internets grand programs are increasingly used, it becomes crucial to describe the traits of robust topologies, and build upcoming networks optimized for robustness. This paper studies the attributes of network topologies that sustain a high level of throughput despite several attacks. To this end, we choose network topologies owned by the major network brands and various real globe networks. Robustness measure is mainly elasticity and, through our analysis, demonstrates diverse topologies that can contain different levels of robustness. In addition, we employ an exchange function that merges elasticity under the three attack policies and regards the rate of the network. Our wide-ranging models indicate that, for a given network, density, semi-regular and regular topologies can have greater extents of robustness than varied topologies. Moreover, link redundancy is an adequate but not essential stipulation for robustness (Sydney, 291).
Was the study experimental or non-experimental?
The study was non-experimental since in involved various variables and there was no differentiation of dependent and independent variable. This study used eighteen different network typologies (Sydney, 294).
Was the research qualitative or quantitative?
The research was quantitative since the study used numerical data. For each topology selected, some of the more common properties of network characteristics quantified into statistical data were the diameter, the average shortest path, and heterogeneity (Sydney, 294).
What was the population studied?
The population, which the researchers studied, was the online social networking, which brings people with common interest together (Sydney, 299).
What sample was used for this study?
This study used a sample of three main networks, which were YouTube, My Space, and Flickr. The researchers attained these networks through snowball sampling and rescaling (Sydney, 299).
If the research was quantitative, was the measurement scale used, Nominal, Ordinal, Interval or Ratio?
The measurement scale that was used in this research was ratio. This is because the researchers measured the robustness of the networks in terms of the elasticity (300). Moreover, they gave each network a grade of one, two or three, according to its value for elasticity: one as the uppermost and three as the lowest (Sydney, 306).
If the research was quantitative, what statistical tools were used to analyze the data?
The major statistical tool used for data analysis was correlation. There was the Correlation linking elasticity and number of links (309), correlation linking elasticity and heterogeneity (312) and the correlation linking elasticity and characteristic path length (Sydney, 316).
What was the conclusion of the study?
This paper attempts to extract the features of robust multifaceted networks. The researchers used elasticity, which computes the capability of a network to uphold its total throughput under escalating removal of nodes with particular links, and hypothetically derived its upper bound as a gauge for robustness. They also derived from the study that elasticity offers benefits, which are far-reaching (Sydney, 319).
Why is this study useful to you? Explain in detail
This study provides a detailed research of the robustness of complex networks, which is useful and relevant to my future career. Network robustness is an area that is very complex and needs a lot of research, in which I aspire to extend. This research therefore, has helped me to gain more insight into the future research.
What would be the next logical step in extending this study?
For our future studies, researchers should integrate expander graphs in their assessment and devise a working definition of the margin and core to comprise details concerning the size and characteristics. They should also endeavor to merge particular graphs to establish the necessary components to boost elasticity (Sydney, 319).