8,164 research outputs found
Functional Complexity Measure for Networks
We propose a complexity measure which addresses the functional flexibility of
networks. It is conjectured that the functional flexibility is reflected in the
topological diversity of the assigned graphs, resulting from a resolution of
their vertices and a rewiring of their edges under certain constraints. The
application will be a classification of networks in artificial or biological
systems, where functionality plays a central role.Comment: 11 pages, LaTeX2e, 5 PostScript figure
Testing robustness of relative complexity measure method constructing robust phylogenetic trees for Galanthus L. Using the relative complexity measure
Background: Most phylogeny analysis methods based on molecular sequences use multiple alignment where the quality of the alignment, which is dependent on the alignment parameters, determines the accuracy of the resulting trees. Different parameter combinations chosen for the multiple alignment may result in different phylogenies. A new non-alignment based approach, Relative Complexity Measure (RCM), has been introduced to tackle this problem and proven to work in fungi and mitochondrial DNA.
Result: In this work, we present an application of the RCM method to reconstruct robust phylogenetic trees using sequence data for genus Galanthus obtained from different regions in Turkey. Phylogenies have been analyzed using nuclear and chloroplast DNA sequences. Results showed that, the tree obtained from nuclear ribosomal RNA gene sequences was more robust, while the tree obtained from the chloroplast DNA showed a higher degree of variation.
Conclusions: Phylogenies generated by Relative Complexity Measure were found to be robust and results of RCM were more reliable than the compared techniques. Particularly, to overcome MSA-based problems, RCM seems to be a reasonable way and a good alternative to MSA-based phylogenetic analysis. We believe our method will become a mainstream phylogeny construction method especially for the highly variable sequence families where the accuracy of the MSA heavily depends on the alignment parameters
WEAK MEASUREMENT THEORY AND MODIFIED COGNITIVE COMPLEXITY MEASURE
Measurement is one of the problems in the area of software engineering. Since traditional measurement
theory has a major problem in defining empirical observations on software entities in terms of their
measured quantities, Morasca has tried to solve this problem by proposing Weak Measurement theory. In
this paper, we tried to evaluate the applicability of weak measurement theory by applying it on a newly
proposed Modified Cognitive Complexity Measure (MCCM). We also investigated the applicability of
Weak Extensive Structure for deciding on the type of scale for MCCM. It is observed that the MCCM is on
weak ratio scale
A Complexity Measure for Continuous Time Quantum Algorithms
We consider unitary dynamical evolutions on n qubits caused by time dependent
pair-interaction Hamiltonians and show that the running time of a parallelized
two-qubit gate network simulating the evolution is given by the time integral
over the chromatic index of the interaction graph. This defines a complexity
measure of continuous and discrete quantum algorithms which are in exact
one-to-one correspondence. Furthermore we prove a lower bound on the growth of
large-scale entanglement depending on the chromatic index.Comment: 6 pages, Revte
Offdiagonal Complexity: A computationally quick complexity measure for graphs and networks
A vast variety of biological, social, and economical networks shows
topologies drastically differing from random graphs; yet the quantitative
characterization remains unsatisfactory from a conceptual point of view.
Motivated from the discussion of small scale-free networks, a biased link
distribution entropy is defined, which takes an extremum for a power law
distribution. This approach is extended to the node-node link
cross-distribution, whose nondiagonal elements characterize the graph structure
beyond link distribution, cluster coefficient and average path length. From
here a simple (and computationally cheap) complexity measure can be defined.
This Offdiagonal Complexity (OdC) is proposed as a novel measure to
characterize the complexity of an undirected graph, or network. While both for
regular lattices and fully connected networks OdC is zero, it takes a
moderately low value for a random graph and shows high values for apparently
complex structures as scale-free networks and hierarchical trees. The
Offdiagonal Complexity apporach is applied to the Helicobacter pylori protein
interaction network and randomly rewired surrogates.Comment: 12 pages, revised version, to appear in Physica
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