27,171 research outputs found
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
Thinking through the implications of neural reuse for the additive factors method
One method for uncovering the subprocesses of mental processes is the “Additive Factors Method” (AFM). The AFM uses reaction time data from factorial experiments to infer the presence of separate processing stages. This paper investigates the conceptual status of the AFM. It argues that one of the AFM’s underlying assumptions is problematic in light of recent developments in cognitive neuroscience. Discussion begins by laying out the basic logic of the AFM, followed by an analysis of the challenge presented by neural reuse. Following this, implications are analysed and avenues of response considered
Development of modularity in the neural activity of children's brains
We study how modularity of the human brain changes as children develop into
adults. Theory suggests that modularity can enhance the response function of a
networked system subject to changing external stimuli. Thus, greater cognitive
performance might be achieved for more modular neural activity, and modularity
might likely increase as children develop. The value of modularity calculated
from fMRI data is observed to increase during childhood development and peak in
young adulthood. Head motion is deconvolved from the fMRI data, and it is shown
that the dependence of modularity on age is independent of the magnitude of
head motion. A model is presented to illustrate how modularity can provide
greater cognitive performance at short times, i.e.\ task switching. A fitness
function is extracted from the model. Quasispecies theory is used to predict
how the average modularity evolves with age, illustrating the increase of
modularity during development from children to adults that arises from
selection for rapid cognitive function in young adults. Experiments exploring
the effect of modularity on cognitive performance are suggested. Modularity may
be a potential biomarker for injury, rehabilitation, or disease.Comment: 29 pages, 11 figure
Modularity Enhances the Rate of Evolution in a Rugged Fitness Landscape
Biological systems are modular, and this modularity affects the evolution of
biological systems over time and in different environments. We here develop a
theory for the dynamics of evolution in a rugged, modular fitness landscape. We
show analytically how horizontal gene transfer couples to the modularity in the
system and leads to more rapid rates of evolution at short times. The model, in
general, analytically demonstrates a selective pressure for the prevalence of
modularity in biology. We use this model to show how the evolution of the
influenza virus is affected by the modularity of the proteins that are
recognized by the human immune system. Approximately 25\% of the observed rate
of fitness increase of the virus could be ascribed to a modular viral
landscape.Comment: 45 pages; 7 figure
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