Sharif Digital Repository

The modern science of networks helps us to have a better understanding of complex systems. Networked systems can be found everywhere and many systems can be represented by a complex network. A networked structure consists of a number of nodes and links connecting to them. Networks’ ability in information propagation is one of their amazing features that have attracted lots of scholars to work on. It has potential applications in many fields ranging from marketing to biology, epidemiology and sociology. Information propagation studies how information such as computer viruses, contagion, rumor, or new product’s interest propagates over a network. Percolation theory and various epidemic models... 

An infectious disease can spread through different communities via mobility networks. In this study we address three basic questions related to this matter in the meta-population approximation: firstly, where did the disease start? Secondly, when did the disease start? Thirdly, how does it spread in the network? To answer these questions, we introduce a generic mathematical framework with appropriate physical assumptions and study the spread of diseases. Then, with analytical solutions, we bring up different algorithms in order to answer these three questions. Using these algorithms, we redefine the effective distance and arriving time and unveil the simple geometry of the disease outbreak  

Networks and the epidemiology of directly transmitted infectious diseases are fun-damentally linked. The foundations of epidemiology and early epidemiological models were based on population wide random-mixing, but in practice each individual has a finite set of contacts to whom they can pass infection; the ensemble of all such contacts forms a network. Knowledge of the structure of the network allows models to compute the epidemic dynamics at the population scale from the individual-level behaviour of infections.Motivated by the analysis of social networks, we study a model of random net-works that has both a given degree distribution and a tunable clustering coefficient.We consider two...