50,079 research outputs found
Uncertainty in phylogenetic tree estimates
Estimating phylogenetic trees is an important problem in evolutionary
biology, environmental policy and medicine. Although trees are estimated, their
uncertainties are discarded by mathematicians working in tree space. Here we
explicitly model the multivariate uncertainty of tree estimates. We consider
both the cases where uncertainty information arises extrinsically (through
covariate information) and intrinsically (through the tree estimates
themselves). The importance of accounting for tree uncertainty in tree space is
demonstrated in two case studies. In the first instance, differences between
gene trees are small relative to their uncertainties, while in the second, the
differences are relatively large. Our main goal is visualization of tree
uncertainty, and we demonstrate advantages of our method with respect to
reproducibility, speed and preservation of topological differences compared to
visualization based on multidimensional scaling. The proposal highlights that
phylogenetic trees are estimated in an extremely high-dimensional space,
resulting in uncertainty information that cannot be discarded. Most
importantly, it is a method that allows biologists to diagnose whether
differences between gene trees are biologically meaningful, or due to
uncertainty in estimation.Comment: Final version accepted to Journal of Computational and Graphical
Statistic
Fast filtering and animation of large dynamic networks
Detecting and visualizing what are the most relevant changes in an evolving
network is an open challenge in several domains. We present a fast algorithm
that filters subsets of the strongest nodes and edges representing an evolving
weighted graph and visualize it by either creating a movie, or by streaming it
to an interactive network visualization tool. The algorithm is an approximation
of exponential sliding time-window that scales linearly with the number of
interactions. We compare the algorithm against rectangular and exponential
sliding time-window methods. Our network filtering algorithm: i) captures
persistent trends in the structure of dynamic weighted networks, ii) smoothens
transitions between the snapshots of dynamic network, and iii) uses limited
memory and processor time. The algorithm is publicly available as open-source
software.Comment: 6 figures, 2 table
Studying the Emerging Global Brain: Analyzing and Visualizing the Impact of Co-Authorship Teams
This paper introduces a suite of approaches and measures to study the impact
of co-authorship teams based on the number of publications and their citations
on a local and global scale. In particular, we present a novel weighted graph
representation that encodes coupled author-paper networks as a weighted
co-authorship graph. This weighted graph representation is applied to a dataset
that captures the emergence of a new field of science and comprises 614 papers
published by 1,036 unique authors between 1974 and 2004. In order to
characterize the properties and evolution of this field we first use four
different measures of centrality to identify the impact of authors. A global
statistical analysis is performed to characterize the distribution of paper
production and paper citations and its correlation with the co-authorship team
size. The size of co-authorship clusters over time is examined. Finally, a
novel local, author-centered measure based on entropy is applied to determine
the global evolution of the field and the identification of the contribution of
a single author's impact across all of its co-authorship relations. A
visualization of the growth of the weighted co-author network and the results
obtained from the statistical analysis indicate a drift towards a more
cooperative, global collaboration process as the main drive in the production
of scientific knowledge.Comment: 13 pages, 9 figure
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