840 research outputs found
An efficient and principled method for detecting communities in networks
A fundamental problem in the analysis of network data is the detection of
network communities, groups of densely interconnected nodes, which may be
overlapping or disjoint. Here we describe a method for finding overlapping
communities based on a principled statistical approach using generative network
models. We show how the method can be implemented using a fast, closed-form
expectation-maximization algorithm that allows us to analyze networks of
millions of nodes in reasonable running times. We test the method both on
real-world networks and on synthetic benchmarks and find that it gives results
competitive with previous methods. We also show that the same approach can be
used to extract nonoverlapping community divisions via a relaxation method, and
demonstrate that the algorithm is competitively fast and accurate for the
nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
Effects of Contact Network Models on Stochastic Epidemic Simulations
The importance of modeling the spread of epidemics through a population has
led to the development of mathematical models for infectious disease
propagation. A number of empirical studies have collected and analyzed data on
contacts between individuals using a variety of sensors. Typically one uses
such data to fit a probabilistic model of network contacts over which a disease
may propagate. In this paper, we investigate the effects of different contact
network models with varying levels of complexity on the outcomes of simulated
epidemics using a stochastic Susceptible-Infectious-Recovered (SIR) model. We
evaluate these network models on six datasets of contacts between people in a
variety of settings. Our results demonstrate that the choice of network model
can have a significant effect on how closely the outcomes of an epidemic
simulation on a simulated network match the outcomes on the actual network
constructed from the sensor data. In particular, preserving degrees of nodes
appears to be much more important than preserving cluster structure for
accurate epidemic simulations.Comment: To appear at International Conference on Social Informatics (SocInfo)
201
Self-regulated charge transfer and band tilt in nm-scale polar GaN films
To date, the generic polarization of Bernardini, Fiorentini and Vanderbilt
(PBFV) has been widely used to address the issue of polarity in III-V nitride
semiconductors, but improvements in nitride materials and the performance of
optoelectronic devices have been limited. The current first-principles
calculation for the electronic structures of nm-scale [0001] GaN films show
that the internal electric fields and the band tilt of these films are in
opposite direction to those predicted by PBFV. Additionally, it is determined
that an intrinsic self-regulated charge transfer across the film limits the
electrostatic potential difference across the film, which renders the local
conduction band energy minimum (at the Ga-terminated surface) approximately
equal to the local valence band energy maximum (at the N-terminated surface).
This effect is found to occur in films thicker than ~4nm
Percolation in the classical blockmodel
Classical blockmodel is known as the simplest among models of networks with
community structure. The model can be also seen as an extremely simply example
of interconnected networks. For this reason, it is surprising that the
percolation transition in the classical blockmodel has not been examined so
far, although the phenomenon has been studied in a variety of much more
complicated models of interconnected and multiplex networks. In this paper we
derive the self-consistent equation for the size the global percolation cluster
in the classical blockmodel. We also find the condition for percolation
threshold which characterizes the emergence of the giant component. We show
that the discussed percolation phenomenon may cause unexpected problems in a
simple optimization process of the multilevel network construction. Numerical
simulations confirm the correctness of our theoretical derivations.Comment: 7 pages, 6 figure
Temporal Evolution of the Solar Wind Bulk Velocity atSolar Minimum by Correlating the STEREO A andBPLASTIC Measurements
The two STEREO spacecraft with nearly identical instrumentation were launched near solar activity minimum and they separate by about 45° per year, providing a unique tool to study the temporal evolution of the solar wind. We analyze the solar wind bulk velocity measured by the two PLASTIC plasma instruments onboard the two STEREO spacecraft. During the first half year of our measurements (Marchâ-âAugust 2007) we find the typical alternating slow and fast solar wind stream pattern expected at solar minimum. To evaluate the temporal evolution of the solar wind bulk velocity we exclude the spatial variations and calculate the correlation between the solar wind bulk velocity measured by the two spacecraft. We account for the different spacecraft positions in radial distance and longitude by calculating the corresponding time lag. After adjusting for this time lag we compare the solar wind bulk velocity measurements at the two spacecraft and calculate the correlation between the two time-shifted datasets. We show how this correlation decreases as the time difference between two corresponding measurements increases. As a result, the characteristic temporal changes in the solar wind bulk velocity can be inferred. The obtained correlation is 0.95 for a time lag of 0.5 days and 0.85 for 2 day
The interplay of microscopic and mesoscopic structure in complex networks
Not all nodes in a network are created equal. Differences and similarities
exist at both individual node and group levels. Disentangling single node from
group properties is crucial for network modeling and structural inference.
Based on unbiased generative probabilistic exponential random graph models and
employing distributive message passing techniques, we present an efficient
algorithm that allows one to separate the contributions of individual nodes and
groups of nodes to the network structure. This leads to improved detection
accuracy of latent class structure in real world data sets compared to models
that focus on group structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the statistical
significance of small subgraph (motif) distributions in networks may be
sufficient to explain most of the observed statistics. We show the predictive
power of such generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man (OMIM) database. The
approach is suitable for both directed and undirected uni-partite as well as
for bipartite networks
Interference with oxidative phosphorylation enhances anoxic expression of rice α-amylase genes through abolishing sugar regulation
Rice has the unique ability to express α-amylase under anoxic conditions, a feature that is critical for successful anaerobic germination and growth. Previously, anaerobic conditions were shown to up-regulate the expression of Amy3 subfamily genes (Amy3B/C, 3D, and 3E) in rice embryos. These genes are known to be feedback regulated by the hydrolytic products of starchy endosperm such as the simple sugar glucose. It was found that oxygen deficiency interferes with the repression of Amy3D gene expression imposed by low concentrations of glucose but not with that imposed by higher amounts. This differential anoxic de-repression depending on sugar concentration suggests the presence of two distinct pathways for sugar regulation of Amy3D gene expression. Anoxic de-repression can be mimicked by treating rice embryos with inhibitors of ATP synthesis during respiration. Other sugar-regulated rice α-amylase genes, Amy3B/C and 3E, behave similarly to Amy3D. Treatment with a respiratory inhibitor or anoxia also relieved the sugar repression of the rice CIPK15 gene, a main upstream positive regulator of SnRK1A that is critical for Amy3D expression in response to sugar starvation. SnRK1A accumulation was previously shown to be required for MYBS1 expression, which transactivates Amy3D by binding to a cis-acting element found in the proximal region of all Amy3 subfamily gene promoters (the TA box). Taken together, these results suggest that prevention of oxidative phosphorylation by oxygen deficiency interferes with the sugar repression of Amy3 subfamily gene expression, leading to their enhanced expression in rice embryos during anaerobic germination
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