Analysis of node based epidemic Spreading processes over Multiplex social networks and Wireless sensor networks

Abstract

The proliferation of epidemic processes (such as malware, memes,rumors, etc.) in the network has attracted substantial attention from the socialscentists, data scientists, and communication engineers to control the outbreakof the epidemic. Epidemic modeling plays a vital role in describing the dynamicbehavior of spreading processes over the network to control and to prevent.Optimal control of epidemics is an emerging research area that mitigates theepidemic spreading in large scale networks. Hence, the analysis of node-based newlineepidemic spreading models should be an appropriate approach for studyingthe dynamic behavior of the epidemic spreading over networks in comparisonwith population-based epidemic models. In this dissertation, the dynamicbehavior of competing memes over multiplex social networks, optimal controlof malware propagation in wireless sensor networks, virus spreading overcomputer networks with partial immunization, and optimal control of competingfor rumor spreading in multiplex social networks has been investigated.The dynamic behaviors of the two competing epidemics (meme)spreading model with the alert of memes over multiplex social networks areanalyzed. Each meme spreads over two distinct Contact Networks (CNs)of the undirected multiplex social networks. The behavioral responses ofagents (alerts) to the spread of competing memes are disseminated throughthe Information Dissemination Network (IDN). The analytical treatment ofthis model is studied through the mean-field approximation of the epidemicprocess. To describe the characteristics of memes, extinction, coexistence, andabsolute dominance are analytically found. Moreover, the co-existence of boththe memes, the survival threshold, the absolute dominance threshold of the two newline newline