Understanding changes in the structure of complex networks using QSAR/QSPR

The Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been used, for many years, to solve different kinds of problems in different domains, such as mathematics, science, and chemistry. The classification-based QSAR/QSPR models represent an important study to predict and understand the changes in complex networks. In this context, the QSAR/QSPR use molecular descriptors as tools to represent and characterize the structure of networks. On the other hand, Complex Networks are useful in analyzing real-world networks which are all around us in chemistry, industry, mathematics, etc. These systems have topological characteristics that don’t appear in simple networks. We can cite, for instance, chemical networks, Technological networks, Biological networks, social networks. Indeed, complex networks have the same representation: vertices (atoms, routers, computers, pages, persons, etc) and edges (chemical bonds, physical links, friendship). To model and understand the changes and relationship between the components in these systems we use numeric parameters called Topological indices that allow us to describe the nature and the connectivity between the entities that represent the complex networks. The goal of this work is to use the topological indices to measure social network analysis by representing the accounts (persons) as vertices and the relationship between them as edge. In this paper, we are going to calculate distance-degree based topological index called the Degree Distance index DD(G) for some particular complex networks connected to each other in different ways.