Some Considerations on Milgram Condition and Degrees of Separation in Complex and Social Networks
Complex networks have played an important role in describing real complex systems since the end of the last century. Since around 1998, studies on social structures and dynamics have begun, and models such as the small-world (SW) and scale-free (SF) models have been proposed. Usually, network connectivity is assumed to be completely regular or completely random. But many biological and social networks fall somewhere between these two categories. There are simple models of so-called "small-world" networks that can be achieved through this middle limit. To achieve this model, starting from regular networks and rewiring between nodes is done. By examining this model, we find that these networks can have a very high clustering coefficient, but the path length in this model is very short. The model of small-world networks is expressed by analogy with the small-world phenomenon (commonly known as six degrees of separation). Neural network, electrical network, and film actor collaboration graph are examples of small world networks. The use of this model leads to an increase in signal propagation speed, computing power and synchronization capability. Also, diseases spread more easily in small world networks than in regular networks. In this research, we intend to comprehensively investigate and simulate this model of networks and study the effect of changes in different parameters on these networks.