48 research outputs found
Mixed coordinate Node link Visualization for Co_authorship Hypergraph Networks
We present an algorithmic technique for visualizing the co-authorship
networks and other networks modeled with hypergraphs (set systems). As more
than two researchers can co-author a paper, a direct representation of the
interaction of researchers through their joint works cannot be adequately
modeled with direct links between the author-nodes. A hypergraph representation
of a co-authorship network treats researchers/authors as nodes and papers as
hyperedges (sets of authors). The visualization algorithm that we propose is
based on one of the well-studied approaches representing both authors and
papers as nodes of different classes. Our approach resembles some known ones
like anchored maps but introduces some special techniques for optimizing the
vertex positioning. The algorithm involves both continuous (force-directed)
optimization and discrete optimization for determining the node coordinates.
Moreover, one of the novelties of this work is classifying nodes and links
using different colors. This usage has a meaningful purpose that helps the
viewer to obtain valuable information from the visualization and increases the
readability of the layout. The algorithm is tuned to enable the viewer to
answer questions specific to co-authorship network studies.Comment: 10 pages, 3 figures, 1 tabl
Refined Multiscale Fuzzy Entropy based on Standard Deviation for Biomedical Signal Analysis
Multiscale entropy (MSE) has been a prevalent algorithm to quantify the
complexity of fluctuations in the local mean value of biomedical time series.
Recent developments in the field have tried to improve the MSE by reducing its
variability in large scale factors. On the other hand, there has been recent
interest in using other statistical moments than the mean, i.e. variance, in
the coarse-graining step of the MSE. Building on these trends, here we
introduce the so-called refined composite multiscale fuzzy entropy based on the
standard deviation (RCMFE{\sigma}) to quantify the dynamical properties of
spread over multiple time scales. We demonstrate the dependency of the
RCMFE{\sigma}, in comparison with other multiscale approaches, on several
straightforward signal processing concepts using a set of synthetic signals. We
also investigate the complementarity of using the standard deviation instead of
the mean in the coarse-graining process using magnetoencephalograms in
Alzheimer disease and publicly available electroencephalograms recorded from
focal and non-focal areas in epilepsy. Our results indicate that RCMFE{\sigma}
offers complementary information to that revealed by classical coarse-graining
approaches and that it has superior performance to distinguish different types
of physiological activity
Entropy Analysis of Univariate Biomedical Signals:Review and Comparison of Methods
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