Comprehending Complexity: Data-Rate Constraints in Large-Scale Networks

ExplodeLayout: Comprehending Patient Subgroups in Large Networks

Networks have been used to successfully identify and comprehend patient subgroups based on their characteristics (e.g., comorbidities or genes) with the goal of designing targeted interventions, a cornerstone of precision medicine. However, current network layout algorithms often fail to reveal patterns in large and dense networks despite having significant clustering. We therefore developed an...

Macroscopic Dynamics in Large-Scale Data Networks

An Efficient Data Replication Strategy in Large-Scale Data Grid Environments Based on Availability and Popularity

The data grid technology, which uses the scale of the Internet to solve storage limitation for the huge amount of data, has become one of the hot research topics. Recently, data replication strategies have been widely employed in distributed environment to copy frequently accessed data in suitable sites. The primary purposes are shortening distance of file transmission and achieving files from ...

Making Large-Scale Networks from fMRI Data

Pairwise correlations are currently a popular way to estimate a large-scale network (> 1000 nodes) from functional magnetic resonance imaging data. However, this approach generally results in a poor representation of the true underlying network. The reason is that pairwise correlations cannot distinguish between direct and indirect connectivity. As a result, pairwise correlation networks can le...

Stability constraints on large-scale structural brain networks

Stability is an important dynamical property of complex systems and underpins a broad range of coherent self-organized behavior. Based on evidence that some neurological disorders correspond to linear instabilities, we hypothesize that stability constrains the brain's electrical activity and influences its structure and physiology. Using a physiologically-based model of brain electrical activit...