2,228 research outputs found
Community Structure Characterization
This entry discusses the problem of describing some communities identified in
a complex network of interest, in a way allowing to interpret them. We suppose
the community structure has already been detected through one of the many
methods proposed in the literature. The question is then to know how to extract
valuable information from this first result, in order to allow human
interpretation. This requires subsequent processing, which we describe in the
rest of this entry
Consensus clustering in complex networks
The community structure of complex networks reveals both their organization
and hidden relationships among their constituents. Most community detection
methods currently available are not deterministic, and their results typically
depend on the specific random seeds, initial conditions and tie-break rules
adopted for their execution. Consensus clustering is used in data analysis to
generate stable results out of a set of partitions delivered by stochastic
methods. Here we show that consensus clustering can be combined with any
existing method in a self-consistent way, enhancing considerably both the
stability and the accuracy of the resulting partitions. This framework is also
particularly suitable to monitor the evolution of community structure in
temporal networks. An application of consensus clustering to a large citation
network of physics papers demonstrates its capability to keep track of the
birth, death and diversification of topics.Comment: 11 pages, 12 figures. Published in Scientific Report
A New Methodology for Generalizing Unweighted Network Measures
Several important complex network measures that helped discovering common
patterns across real-world networks ignore edge weights, an important
information in real-world networks. We propose a new methodology for
generalizing measures of unweighted networks through a generalization of the
cardinality concept of a set of weights. The key observation here is that many
measures of unweighted networks use the cardinality (the size) of some subset
of edges in their computation. For example, the node degree is the number of
edges incident to a node. We define the effective cardinality, a new metric
that quantifies how many edges are effectively being used, assuming that an
edge's weight reflects the amount of interaction across that edge. We prove
that a generalized measure, using our method, reduces to the original
unweighted measure if there is no disparity between weights, which ensures that
the laws that govern the original unweighted measure will also govern the
generalized measure when the weights are equal. We also prove that our
generalization ensures a partial ordering (among sets of weighted edges) that
is consistent with the original unweighted measure, unlike previously developed
generalizations. We illustrate the applicability of our method by generalizing
four unweighted network measures. As a case study, we analyze four real-world
weighted networks using our generalized degree and clustering coefficient. The
analysis shows that the generalized degree distribution is consistent with the
power-law hypothesis but with steeper decline and that there is a common
pattern governing the ratio between the generalized degree and the traditional
degree. The analysis also shows that nodes with more uniform weights tend to
cluster with nodes that also have more uniform weights among themselves.Comment: 23 pages, 10 figure
Why lose weight? Reasons for seeking weight loss by overweight but otherwise healthy men
OBJECTIVE: To identify the reasons for seeking weight loss in overweight or obese but otherwise healthy men.
DESIGN: Interviews, prior to intervention, with subjects who had volunteered to participate in a work-site-based weight loss study.
SUBJECTS: Ninety-one overweight=obese male workers. Mean age 41, range 18 – 55 y, mean body mass index (BMI) 31.0,
range 26.2 – 41.6 kg=m2.
MEASUREMENTS: Anthropometric measurements; body weight and height. Body mass index calculated. A short interview using open questions to determine the individuals reason for seeking weight loss.
RESULTS: The message that weight loss is beneficial to health for the overweight was recognized by all subjects regardless of BMI, and was reported as the main factor for attempting weight loss. Improved fitness and effects on appearance and well-being were reported half as often as the primary reason for weight loss.
CONCLUSION: Overweight lay members of the public have accepted the health education message that weight loss can improve health. Overweight but otherwise healthy men who responded, of their own accord, to an electronic mail message offering help to lose weight did not regard obesity and overweight as primarily a cosmetic issue. This is still, however, important, especially to younger people
Searching for network modules
When analyzing complex networks a key target is to uncover their modular
structure, which means searching for a family of modules, namely node subsets
spanning each a subnetwork more densely connected than the average. This work
proposes a novel type of objective function for graph clustering, in the form
of a multilinear polynomial whose coefficients are determined by network
topology. It may be thought of as a potential function, to be maximized, taking
its values on fuzzy clusterings or families of fuzzy subsets of nodes over
which every node distributes a unit membership. When suitably parametrized,
this potential is shown to attain its maximum when every node concentrates its
all unit membership on some module. The output thus is a partition, while the
original discrete optimization problem is turned into a continuous version
allowing to conceive alternative search strategies. The instance of the problem
being a pseudo-Boolean function assigning real-valued cluster scores to node
subsets, modularity maximization is employed to exemplify a so-called quadratic
form, in that the scores of singletons and pairs also fully determine the
scores of larger clusters, while the resulting multilinear polynomial potential
function has degree 2. After considering further quadratic instances, different
from modularity and obtained by interpreting network topology in alternative
manners, a greedy local-search strategy for the continuous framework is
analytically compared with an existing greedy agglomerative procedure for the
discrete case. Overlapping is finally discussed in terms of multiple runs, i.e.
several local searches with different initializations.Comment: 10 page
Video Pandemics: Worldwide Viral Spreading of Psy's Gangnam Style Video
Viral videos can reach global penetration traveling through international
channels of communication similarly to real diseases starting from a
well-localized source. In past centuries, disease fronts propagated in a
concentric spatial fashion from the the source of the outbreak via the short
range human contact network. The emergence of long-distance air-travel changed
these ancient patterns. However, recently, Brockmann and Helbing have shown
that concentric propagation waves can be reinstated if propagation time and
distance is measured in the flight-time and travel volume weighted underlying
air-travel network. Here, we adopt this method for the analysis of viral meme
propagation in Twitter messages, and define a similar weighted network distance
in the communication network connecting countries and states of the World. We
recover a wave-like behavior on average and assess the randomizing effect of
non-locality of spreading. We show that similar result can be recovered from
Google Trends data as well.Comment: 10 page
Optical alignment and spinning of laser-trapped microscopic particles
Light-induced rotation of absorbing microscopic particles by transfer of
angular momentum from light to the material raises the possibility of optically
driven micromachines. The phenomenon has been observed using elliptically
polarized laser beams or beams with helical phase structure. But it is
difficult to develop high power in such experiments because of overheating and
unwanted axial forces, limiting the achievable rotation rates to a few hertz.
This problem can in principle be overcome by using transparent particles,
transferring angular momentum by a mechanism first observed by Beth in 1936,
when he reported a tiny torque developed in a quartz waveplate due to the
change in polarization of transmitted light. Here we show that an optical
torque can be induced on microscopic birefringent particles of calcite held by
optical tweezers. Depending on the polarization of the incident beam, the
particles either become aligned with the plane of polarization (and thus can be
rotated through specified angles) or spin with constant rotation frequency.
Because these microscopic particles are transparent, they can be held in
three-dimensional optical traps at very high power without heating. We have
observed rotation rates in excess of 350 Hz.Comment: 4 pages, 4 figure
Evolving Clustered Random Networks
We propose a Markov chain simulation method to generate simple connected
random graphs with a specified degree sequence and level of clustering. The
networks generated by our algorithm are random in all other respects and can
thus serve as generic models for studying the impacts of degree distributions
and clustering on dynamical processes as well as null models for detecting
other structural properties in empirical networks
From sparse to dense and from assortative to disassortative in online social networks
Inspired by the analysis of several empirical online social networks, we
propose a simple reaction-diffusion-like coevolving model, in which individuals
are activated to create links based on their states, influenced by local
dynamics and their own intention. It is shown that the model can reproduce the
remarkable properties observed in empirical online social networks; in
particular, the assortative coefficients are neutral or negative, and the power
law exponents are smaller than 2. Moreover, we demonstrate that, under
appropriate conditions, the model network naturally makes transition(s) from
assortative to disassortative, and from sparse to dense in their
characteristics. The model is useful in understanding the formation and
evolution of online social networks.Comment: 10 pages, 7 figures and 2 table
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