4,958 research outputs found
A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks
Recently much attention has been devoted to the construction of phylogenetic
networks which generalize phylogenetic trees in order to accommodate complex
evolutionary processes. Here we present an efficient, practical algorithm for
reconstructing level-1 phylogenetic networks - a type of network slightly more
general than a phylogenetic tree - from triplets. Our algorithm has been made
publicly available as the program LEV1ATHAN. It combines ideas from several
known theoretical algorithms for phylogenetic tree and network reconstruction
with two novel subroutines. Namely, an exponential-time exact and a greedy
algorithm both of which are of independent theoretical interest. Most
importantly, LEV1ATHAN runs in polynomial time and always constructs a level-1
network. If the data is consistent with a phylogenetic tree, then the algorithm
constructs such a tree. Moreover, if the input triplet set is dense and, in
addition, is fully consistent with some level-1 network, it will find such a
network. The potential of LEV1ATHAN is explored by means of an extensive
simulation study and a biological data set. One of our conclusions is that
LEV1ATHAN is able to construct networks consistent with a high percentage of
input triplets, even when these input triplets are affected by a low to
moderate level of noise
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
Networking - A Statistical Physics Perspective
Efficient networking has a substantial economic and societal impact in a
broad range of areas including transportation systems, wired and wireless
communications and a range of Internet applications. As transportation and
communication networks become increasingly more complex, the ever increasing
demand for congestion control, higher traffic capacity, quality of service,
robustness and reduced energy consumption require new tools and methods to meet
these conflicting requirements. The new methodology should serve for gaining
better understanding of the properties of networking systems at the macroscopic
level, as well as for the development of new principled optimization and
management algorithms at the microscopic level. Methods of statistical physics
seem best placed to provide new approaches as they have been developed
specifically to deal with non-linear large scale systems. This paper aims at
presenting an overview of tools and methods that have been developed within the
statistical physics community and that can be readily applied to address the
emerging problems in networking. These include diffusion processes, methods
from disordered systems and polymer physics, probabilistic inference, which
have direct relevance to network routing, file and frequency distribution, the
exploration of network structures and vulnerability, and various other
practical networking applications.Comment: (Review article) 71 pages, 14 figure
The performance of modularity maximization in practical contexts
Although widely used in practice, the behavior and accuracy of the popular
module identification technique called modularity maximization is not well
understood in practical contexts. Here, we present a broad characterization of
its performance in such situations. First, we revisit and clarify the
resolution limit phenomenon for modularity maximization. Second, we show that
the modularity function Q exhibits extreme degeneracies: it typically admits an
exponential number of distinct high-scoring solutions and typically lacks a
clear global maximum. Third, we derive the limiting behavior of the maximum
modularity Q_max for one model of infinitely modular networks, showing that it
depends strongly both on the size of the network and on the number of modules
it contains. Finally, using three real-world metabolic networks as examples, we
show that the degenerate solutions can fundamentally disagree on many, but not
all, partition properties such as the composition of the largest modules and
the distribution of module sizes. These results imply that the output of any
modularity maximization procedure should be interpreted cautiously in
scientific contexts. They also explain why many heuristics are often successful
at finding high-scoring partitions in practice and why different heuristics can
disagree on the modular structure of the same network. We conclude by
discussing avenues for mitigating some of these behaviors, such as combining
information from many degenerate solutions or using generative models.Comment: 20 pages, 14 figures, 6 appendices; code available at
http://www.santafe.edu/~aaronc/modularity
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