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Braided rivers form some of the most fascinating fluvial patterns found on Earth. They are identifiable by their unique morphology of complex networks of intertwined channels that spread across wide floodplains. Detailed knowledge of their dynamics is need ...
In this article, we show how postphenomenology can be used to analyze a visual method that reveals the hidden dynamics that exist between individuals within large organizations. We make use of the Affinity Map to expand the classic postphenomenology that p ...
Braided rivers are highly dynamical systems characterized by varying network-like structures even under quasi-steady conditions. Understanding their dynamics is crucial in geomorphology and river engineering (e.g., river restoration in Alpine and piedmond ...
This article introduces a new class of models for multiple networks. The core idea is to parameterize a distribution on labeled graphs in terms of a Frechet mean graph (which depends on a user-specified choice of metric or graph distance) and a parameter t ...
Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A n ...
A brief session of rightward prismatic adaptation (R-PA) has been shown to alleviate neglect symptoms in patients with right hemispheric damage, very likely by switching hemispheric dominance of the ventral attentional network (VAN) from the right to the l ...
We consider the general problem of describing the dynamics of subnetworks of larger biochemical reaction networks, e.g., protein interaction networks involving complex formation and dissociation reactions. We propose the use of model reduction strategies t ...
We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order ...
The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing ...
In Environmental Impact Assessment (EIA), the either positive or negative impacts that specific N project actions might generate on a number M of environmental components are typically summarized in the form of Interaction Matrix (IM). This is an NxM tabul ...