Transitivity is one of the most important mechanisms to form a social network in a human society, but it remains unclear how such behavior is quantified and affected by some key factors, including the social environment and the participants' characters. This study investigates the sharing behavior based on the notion of transitivity and an experiment is conducted on a variety of populations from kindergarteners to teenagers. The key effects that have high impacts to the sharing behavior are identified from the statistical analysis of the experimental results. A mathematical model is built for the experimental results and its performance compared with other models is also illustrated.
Although COVID-19 has caused severe suffering globally, the efficacy of non-pharmaceutical interventions has been greater than typical models have predicted. Meanwhile, evidence is mounting that the pandemic is characterized by superspreading. Capturing this phenomenon theoretically requires modeling at the scale of individuals. Using a mathematical model, we show that superspreading drastically enhances mitigations which reduce the overall personal contact number, and that social clustering (”social bubbles”) increases this effect.
In this paper I am going to first explain in detail the role of Game Theory over Social Network Analysis. Then I will look into the Predictive model of Artificial Neural network & will explain in details that how this model will be used to develop a mathematical model which will fairly and efficiently allocate the required rate of bandwidth to all the users in a Multiuser Network System. Afterwards, I will propose some newly designed algorithms which will help me in the implementation of the mathematical model. The testing result of the implementation will compare our proposed architecture with the existing model. Finally, I will end this discussion by self-estimating our proposed model and judging the future scope of the same.
A Statistical Evaluation and Modeling on the Social Transitivity Behavior