182,640 research outputs found
Efficient method for estimating the number of communities in a network
While there exist a wide range of effective methods for community detection
in networks, most of them require one to know in advance how many communities
one is looking for. Here we present a method for estimating the number of
communities in a network using a combination of Bayesian inference with a novel
prior and an efficient Monte Carlo sampling scheme. We test the method
extensively on both real and computer-generated networks, showing that it
performs accurately and consistently, even in cases where groups are widely
varying in size or structure.Comment: 13 pages, 4 figure
Estimating the number of communities in weighted networks
Community detection in weighted networks has been a popular topic in recent
years. However, while there exist several flexible methods for estimating
communities in weighted networks, these methods usually assume that the number
of communities is known. It is usually unclear how to determine the exact
number of communities one should use. Here, to estimate the number of
communities for weighted networks generated from arbitrary distribution under
the degree-corrected distribution-free model, we propose one approach that
combines weighted modularity with spectral clustering. This approach allows a
weighted network to have negative edge weights and it also works for signed
networks. We compare the proposed method to several existing methods and show
that our method is more accurate for estimating the number of communities both
numerically and empirically
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Design of gas micro distribution systems consisting of long tubes
This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.A novel algorithm is developed for the design of gaseous micro distribution systems consisting of long tubes based on linear kinetic theory. Provided that the geometry of the pipe network is fixed the algorithm is capable of estimating the mass flow rates through the pipes as well as the pressure heads at the nodes of the network. The pressure distribution along each pipe element may also be provided. The analysis is valid and the results are accurate in the whole range of the Knudsen number, while the involved computational effort is very small. This is achieved by successfully integrating the well known kinetic results for single tubes into a typical solver for designing gas pipe networks.The European Communities under the contract of Association EURATOM / Hellenic Republic
Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks
We investigate the relationship of resting-state fMRI functional connectivity
estimated over long periods of time with time-varying functional connectivity
estimated over shorter time intervals. We show that using Pearson's correlation
to estimate functional connectivity implies that the range of fluctuations of
functional connections over short time scales is subject to statistical
constraints imposed by their connectivity strength over longer scales. We
present a method for estimating time-varying functional connectivity that is
designed to mitigate this issue and allows us to identify episodes where
functional connections are unexpectedly strong or weak. We apply this method to
data recorded from participants, and show that the number of
unexpectedly strong/weak connections fluctuates over time, and that these
variations coincide with intermittent periods of high and low modularity in
time-varying functional connectivity. We also find that during periods of
relative quiescence regions associated with default mode network tend to join
communities with attentional, control, and primary sensory systems. In
contrast, during periods where many connections are unexpectedly strong/weak,
default mode regions dissociate and form distinct modules. Finally, we go on to
show that, while all functional connections can at times manifest stronger
(more positively correlated) or weaker (more negatively correlated) than
expected, a small number of connections, mostly within the visual and
somatomotor networks, do so a disproportional number of times. Our statistical
approach allows the detection of functional connections that fluctuate more or
less than expected based on their long-time averages and may be of use in
future studies characterizing the spatio-temporal patterns of time-varying
functional connectivityComment: 47 Pages, 8 Figures, 4 Supplementary Figure
Selecting a significance level in sequential testing procedures for community detection
While there have been numerous sequential algorithms developed to estimate
community structure in networks, there is little available guidance and study
of what significance level or stopping parameter to use in these sequential
testing procedures. Most algorithms rely on prespecifiying the number of
communities or use an arbitrary stopping rule. We provide a principled approach
to selecting a nominal significance level for sequential community detection
procedures by controlling the tolerance ratio, defined as the ratio of
underfitting and overfitting probability of estimating the number of clusters
in fitting a network. We introduce an algorithm for specifying this
significance level from a user-specified tolerance ratio, and demonstrate its
utility with a sequential modularity maximization approach in a stochastic
block model framework. We evaluate the performance of the proposed algorithm
through extensive simulations and demonstrate its utility in controlling the
tolerance ratio in single-cell RNA sequencing clustering by cell type and by
clustering a congressional voting network.Comment: 16 pages, 1 figur
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