1,827 research outputs found
Multi-scale analysis of the European airspace using network community detection
We show that the European airspace can be represented as a multi-scale
traffic network whose nodes are airports, sectors, or navigation points and
links are defined and weighted according to the traffic of flights between the
nodes. By using a unique database of the air traffic in the European airspace,
we investigate the architecture of these networks with a special emphasis on
their community structure. We propose that unsupervised network community
detection algorithms can be used to monitor the current use of the airspaces
and improve it by guiding the design of new ones. Specifically, we compare the
performance of three community detection algorithms, also by using a null model
which takes into account the spatial distance between nodes, and we discuss
their ability to find communities that could be used to define new control
units of the airspace.Comment: 22 pages, 14 figure
Communities in Networks
We survey some of the concepts, methods, and applications of community
detection, which has become an increasingly important area of network science.
To help ease newcomers into the field, we provide a guide to available
methodology and open problems, and discuss why scientists from diverse
backgrounds are interested in these problems. As a running theme, we emphasize
the connections of community detection to problems in statistical physics and
computational optimization.Comment: survey/review article on community structure in networks; published
version is available at
http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd
Model selection and hypothesis testing for large-scale network models with overlapping groups
The effort to understand network systems in increasing detail has resulted in
a diversity of methods designed to extract their large-scale structure from
data. Unfortunately, many of these methods yield diverging descriptions of the
same network, making both the comparison and understanding of their results a
difficult challenge. A possible solution to this outstanding issue is to shift
the focus away from ad hoc methods and move towards more principled approaches
based on statistical inference of generative models. As a result, we face
instead the more well-defined task of selecting between competing generative
processes, which can be done under a unified probabilistic framework. Here, we
consider the comparison between a variety of generative models including
features such as degree correction, where nodes with arbitrary degrees can
belong to the same group, and community overlap, where nodes are allowed to
belong to more than one group. Because such model variants possess an
increasing number of parameters, they become prone to overfitting. In this
work, we present a method of model selection based on the minimum description
length criterion and posterior odds ratios that is capable of fully accounting
for the increased degrees of freedom of the larger models, and selects the best
one according to the statistical evidence available in the data. In applying
this method to many empirical unweighted networks from different fields, we
observe that community overlap is very often not supported by statistical
evidence and is selected as a better model only for a minority of them. On the
other hand, we find that degree correction tends to be almost universally
favored by the available data, implying that intrinsic node proprieties (as
opposed to group properties) are often an essential ingredient of network
formation.Comment: 20 pages,7 figures, 1 tabl
The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market
The topological properties of interbank networks have been discussed widely
in the literature mainly because of their relevance for systemic risk. Here we
propose to use the Stochastic Block Model to investigate and perform a model
selection among several possible two block organizations of the network: these
include bipartite, core-periphery, and modular structures. We apply our method
to the e-MID interbank market in the period 2010-2014 and we show that in
normal conditions the most likely network organization is a bipartite
structure. In exceptional conditions, such as after LTRO, one of the most
important unconventional measures by ECB at the beginning of 2012, the most
likely structure becomes a random one and only in 2014 the e-MID market went
back to a normal bipartite organization. By investigating the strategy of
individual banks, we explore possible explanations and we show that the
disappearance of many lending banks and the strategy switch of a very small set
of banks from borrower to lender is likely at the origin of this structural
change.Comment: 33 pages, 5 figure
Element-centric clustering comparison unifies overlaps and hierarchy
Clustering is one of the most universal approaches for understanding complex
data. A pivotal aspect of clustering analysis is quantitatively comparing
clusterings; clustering comparison is the basis for many tasks such as
clustering evaluation, consensus clustering, and tracking the temporal
evolution of clusters. In particular, the extrinsic evaluation of clustering
methods requires comparing the uncovered clusterings to planted clusterings or
known metadata. Yet, as we demonstrate, existing clustering comparison measures
have critical biases which undermine their usefulness, and no measure
accommodates both overlapping and hierarchical clusterings. Here we unify the
comparison of disjoint, overlapping, and hierarchically structured clusterings
by proposing a new element-centric framework: elements are compared based on
the relationships induced by the cluster structure, as opposed to the
traditional cluster-centric philosophy. We demonstrate that, in contrast to
standard clustering similarity measures, our framework does not suffer from
critical biases and naturally provides unique insights into how the clusterings
differ. We illustrate the strengths of our framework by revealing new insights
into the organization of clusters in two applications: the improved
classification of schizophrenia based on the overlapping and hierarchical
community structure of fMRI brain networks, and the disentanglement of various
social homophily factors in Facebook social networks. The universality of
clustering suggests far-reaching impact of our framework throughout all areas
of science
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