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Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network(More)
—Component frameworks are complex systems that rely on many layers of abstraction to function properly. One essential requirement is a consistent means of describing each individual component and how it relates to both other components and the whole framework. As component frameworks are designed to be flexible by nature, the description method should be(More)
Spatial variations in the distribution and composition of populations inform urban development, health-risk analyses, disaster relief, and more. Despite the broad relevance and importance of such data, acquiring local census estimates in a timely and accurate manner is challenging because population counts can change rapidly, are often politically charged,(More)
Topological data analysis is a new approach to analyzing the structure of high dimensional datasets. Persistent homology, specifically, generalizes hierarchical clustering methods to identify significant higher dimensional properties. In this project, we analyze mobile network data from Senegal to determine whether significant topological structure is(More)
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