Modelling of disease and information dynamics in complex networks

Abstract

Real-world systems, such as the Internet, World Wide Web, communication, human newlineinteraction, disease spreading in humans, transportation, power grid, and so on, maybe newlinerepresented as networks. Networks are composed of nodes, and a link can be formed newlinebetween two nodes. Initially, the number of connections was thought to be static, but newlinein real-world systems, linkages evolve. As a result, in order to analyze big and current newlinestochastic networks, it is necessary to move beyond the simple network depiction. These newlinenetworks have nontrivial characteristics that should be investigated. The characteristics of newlinea topological network differ from each other if they are neither random nor regular. Some newlineof these networks follow heavy-tailed degree distribution as well as poison distribution, newlinehigh clustering coefficient, community structure, and exhibit correlation among nodes. The human contact network is an example of such a network. Many people move from one newlinelocation to multiple locations, and during their movement, they make connections with newlineother people when people come in under the connectivity region. In order to describe newlinethe physical structure of the network, several network characteristics are used, such as degree distribution, average path length, clustering coefficient, diameter, and degree-degree correlation. These features must be tested against existing real-world systems to evaluate the developed network model. newline