Optimizing Functional Network Representation of Multivariate Time Series
Massimiliano Zanin,Massimiliano Zanin,Pedro Sousa,David Papo,Ricardo Bajo,Juan García-Prieto,Francisco del Pozo,Ernestina Menasalvas,Stefano Boccaletti +8 more
TL;DR: By combining complex network theory and data mining techniques, this work proposes a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes.
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Abstract: By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks
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