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Title Modeling and Refinement of Spreading Processes over Complex Networks
Degree Ph.D.
Author Aresh Dadlani
Advisor Kiseon Kim
Graduation Date 2015.08.25 File
    Date 2016-10-25 11:38
The proliferation of networks in almost every domain has brought about a need
for better understanding, design, and management of their underlying topological characteristics.
In the realm of network science, there exist many seminal works that shed
light on the existing correlation between network connectivity patterns and intriguing
dynamics of spreading processes such as social opinions, malicious computer codes, contagious
diseases, product advertisements, gene mutation, and fashion trends. Finding
roots in epidemiology, a variety of epidemic models have been adopted to scrutinize the
dynamic behavior of such meme propagation over complex networked systems. Scarcity
of recorded data and sensitive dependence of results on assumptions made during modeling
further add to the significance of epidemic modeling paradigms. While often time
and resources limit our ability to comprehend the detailed relationship between network
structures and spread dynamics, the scope of most studies on spreading phenomena has
been confined to basic epidemic models concerning single networks with trivial topologies
and connectivity distributions. Nonetheless, at the core of epidemic modeling over
complex networks lie many challenging issues that require further investigation.
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