187 research outputs found
Detection of node group membership in networks with group overlap
Most networks found in social and biochemical systems have modular
structures. An important question prompted by the modularity of these networks
is whether nodes can be said to belong to a single group. If they cannot, we
would need to consider the role of "overlapping communities." Despite some
efforts in this direction, the problem of detecting overlapping groups remains
unsolved because there is neither a formal definition of overlapping community,
nor an ensemble of networks with which to test the performance of group
detection algorithms when nodes can belong to more than one group. Here, we
introduce an ensemble of networks with overlapping groups. We then apply three
group identification methods--modularity maximization, k-clique percolation,
and modularity-landscape surveying--to these networks. We find that the
modularity-landscape surveying method is the only one able to detect
heterogeneities in node memberships, and that those heterogeneities are only
detectable when the overlap is small. Surprisingly, we find that the k-clique
percolation method is unable to detect node membership for the overlapping
case.Comment: 12 pages, 6 figures. To appear in Euro. Phys. J
Modularity from Fluctuations in Random Graphs and Complex Networks
The mechanisms by which modularity emerges in complex networks are not well
understood but recent reports have suggested that modularity may arise from
evolutionary selection. We show that finding the modularity of a network is
analogous to finding the ground-state energy of a spin system. Moreover, we
demonstrate that, due to fluctuations, stochastic network models give rise to
modular networks. Specifically, we show both numerically and analytically that
random graphs and scale-free networks have modularity. We argue that this fact
must be taken into consideration to define statistically-significant modularity
in complex networks.Comment: 4 page
The Distribution of the Asymptotic Number of Citations to Sets of Publications by a Researcher or From an Academic Department Are Consistent With a Discrete Lognormal Model
How to quantify the impact of a researcher's or an institution's body of work
is a matter of increasing importance to scientists, funding agencies, and
hiring committees. The use of bibliometric indicators, such as the h-index or
the Journal Impact Factor, have become widespread despite their known
limitations. We argue that most existing bibliometric indicators are
inconsistent, biased, and, worst of all, susceptible to manipulation. Here, we
pursue a principled approach to the development of an indicator to quantify the
scientific impact of both individual researchers and research institutions
grounded on the functional form of the distribution of the asymptotic number of
citations. We validate our approach using the publication records of 1,283
researchers from seven scientific and engineering disciplines and the chemistry
departments at the 106 U.S. research institutions classified as "very high
research activity". Our approach has three distinct advantages. First, it
accurately captures the overall scientific impact of researchers at all career
stages, as measured by asymptotic citation counts. Second, unlike other
measures, our indicator is resistant to manipulation and rewards publication
quality over quantity. Third, our approach captures the time-evolution of the
scientific impact of research institutions.Comment: 20 pages, 11 figures, 3 table
Robust Patterns in Food Web Structure
We analyze the properties of seven community food webs from a variety of
environments--including freshwater, marine-freshwater interfaces and
terrestrial environments. We uncover quantitative unifying patterns that
describe the properties of the diverse trophic webs considered and suggest that
statistical physics concepts such as scaling and universality may be useful in
the description of ecosystems. Specifically, we find that several quantities
characterizing these diverse food webs obey functional forms that are universal
across the different environments considered. The empirical results are in
remarkable agreement with the analytical solution of a recently proposed model
for food webs.Comment: 4 pages. Final version to appear in PR
A high-reproducibility and high-accuracy method for automated topic classification
Much of human knowledge sits in large databases of unstructured text.
Leveraging this knowledge requires algorithms that extract and record metadata
on unstructured text documents. Assigning topics to documents will enable
intelligent search, statistical characterization, and meaningful
classification. Latent Dirichlet allocation (LDA) is the state-of-the-art in
topic classification. Here, we perform a systematic theoretical and numerical
analysis that demonstrates that current optimization techniques for LDA often
yield results which are not accurate in inferring the most suitable model
parameters. Adapting approaches for community detection in networks, we propose
a new algorithm which displays high-reproducibility and high-accuracy, and also
has high computational efficiency. We apply it to a large set of documents in
the English Wikipedia and reveal its hierarchical structure. Our algorithm
promises to make "big data" text analysis systems more reliable.Comment: 23 pages, 24 figure
Canalizing Kauffman networks: non-ergodicity and its effect on their critical behavior
Boolean Networks have been used to study numerous phenomena, including gene
regulation, neural networks, social interactions, and biological evolution.
Here, we propose a general method for determining the critical behavior of
Boolean systems built from arbitrary ensembles of Boolean functions. In
particular, we solve the critical condition for systems of units operating
according to canalizing functions and present strong numerical evidence that
our approach correctly predicts the phase transition from order to chaos in
such systems.Comment: to be published in PR
Comment on: Kinetic Roughening in Slow Combustion of Paper
We comment on a recent Letter by Maunuksela et al. [Phys. Rev. Lett. 79, 1515
(1997)].Comment: 1 page, 1 figure, http://polymer.bu.edu/~hmakse/Home.htm
Energy avalanches in a rice-pile model
We investigate a one-dimensional rice-pile model. We show that the
distribution of dissipated potential energy decays as a power law with an
exponent . The system thus provides a one-dimensional example of
self-organized criticality. Different driving conditions are examined in order
to allow for comparison with experiments.Comment: 8 pages, elsart sty files (provided
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