171 research outputs found
Explosive percolation in graphs
Percolation is perhaps the simplest example of a process exhibiting a phase
transition and one of the most studied phenomena in statistical physics. The
percolation transition is continuous if sites/bonds are occupied independently
with the same probability. However, alternative rules for the occupation of
sites/bonds might affect the order of the transition. A recent set of rules
proposed by Achlioptas et al. [Science 323, 1453 (2009)], characterized by
competitive link addition, was claimed to lead to a discontinuous connectedness
transition, named "explosive percolation". In this work we survey a numerical
study of the explosive percolation transition on various types of graphs, from
lattices to scale-free networks, and show the consistency of these results with
recent analytical work showing that the transition is actually continuous.Comment: 10 pages, 7 figures, 1 table. Contribution to the Proceedings of
STATPHYS-Kolkata VII, November 26-30, 201
Analyzing Ideological Communities in Congressional Voting Networks
We here study the behavior of political party members aiming at identifying
how ideological communities are created and evolve over time in diverse
(fragmented and non-fragmented) party systems. Using public voting data of both
Brazil and the US, we propose a methodology to identify and characterize
ideological communities, their member polarization, and how such communities
evolve over time, covering a 15-year period. Our results reveal very distinct
patterns across the two case studies, in terms of both structural and dynamic
properties
Router-level community structure of the Internet Autonomous Systems
The Internet is composed of routing devices connected between them and
organized into independent administrative entities: the Autonomous Systems. The
existence of different types of Autonomous Systems (like large connectivity
providers, Internet Service Providers or universities) together with
geographical and economical constraints, turns the Internet into a complex
modular and hierarchical network. This organization is reflected in many
properties of the Internet topology, like its high degree of clustering and its
robustness.
In this work, we study the modular structure of the Internet router-level
graph in order to assess to what extent the Autonomous Systems satisfy some of
the known notions of community structure. We show that the modular structure of
the Internet is much richer than what can be captured by the current community
detection methods, which are severely affected by resolution limits and by the
heterogeneity of the Autonomous Systems. Here we overcome this issue by using a
multiresolution detection algorithm combined with a small sample of nodes. We
also discuss recent work on community structure in the light of our results
Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data
Determining the functional structure of biological networks is a central goal
of systems biology. One approach is to analyze gene expression data to infer a
network of gene interactions on the basis of their correlated responses to
environmental and genetic perturbations. The inferred network can then be
analyzed to identify functional communities. However, commonly used algorithms
can yield unreliable results due to experimental noise, algorithmic
stochasticity, and the influence of arbitrarily chosen parameter values.
Furthermore, the results obtained typically provide only a simplistic view of
the network partitioned into disjoint communities and provide no information of
the relationship between communities. Here, we present methods to robustly
detect coregulated and functionally enriched gene communities and demonstrate
their application and validity for Escherichia coli gene expression data.
Applying a recently developed community detection algorithm to the network of
interactions identified with the context likelihood of relatedness (CLR)
method, we show that a hierarchy of network communities can be identified.
These communities significantly enrich for gene ontology (GO) terms, consistent
with them representing biologically meaningful groups. Further, analysis of the
most significantly enriched communities identified several candidate new
regulatory interactions. The robustness of our methods is demonstrated by
showing that a core set of functional communities is reliably found when
artificial noise, modeling experimental noise, is added to the data. We find
that noise mainly acts conservatively, increasing the relatedness required for
a network link to be reliably assigned and decreasing the size of the core
communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1
was not uploaded but is available by contacting the author. 27 pages, 5
figures, 15 supplementary file
Robust detection of communities with multi-semantics in large attributed networks
© 2018, Springer Nature Switzerland AG. In this paper, we are interested in how to explore and utilize the relationship between network communities and semantic topics in order to find the strong explanatory communities robustly. First, the relationship between communities and topics displays different situations. For example, from the viewpoint of semantic mapping, their relationship can be one-to-one, one-to-many or many-to-one. But from the standpoint of underlying community structures, the relationship can be consistent, partially consistent or completely inconsistent. Second, it will be helpful to not only find communities more precise but also reveal the communities’ semantics that shows the relationship between communities and topics. To better describe this relationship, we introduce the transition probability which is an important concept in Markov chain into a well-designed nonnegative matrix factorization framework. This new transition probability matrix with a suitable prior which plays the role of depicting the relationship between communities and topics can perform well in this task. To illustrate the effectiveness of the proposed new approach, we conduct some experiments on both synthetic and real networks. The results show that our new method is superior to baselines in accuracy. We finally conduct a case study analysis to validate the new method’s strong interpretability to detected communities
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
Cohesive strength of nanocrystalline ZnO:Ga thin films deposited at room temperature
In this study, transparent conducting nanocrystalline ZnO:Ga (GZO) films were deposited by dc magnetron sputtering at room temperature on polymers (and glass for comparison). Electrical resistivities of 8.8 × 10-4 and 2.2 × 10-3 Ω cm were obtained for films deposited on glass and polymers, respectively. The crack onset strain (COS) and the cohesive strength of the coatings were investigated by means of tensile testing. The COS is similar for different GZO coatings and occurs for nominal strains approx. 1%. The cohesive strength of coatings, which was evaluated from the initial part of the crack density evolution, was found to be between 1.3 and 1.4 GPa. For these calculations, a Young's modulus of 112 GPa was used, evaluated by nanoindentation
Asymptotic theory of time-varying social networks with heterogeneous activity and tie allocation
The dynamic of social networks is driven by the interplay between diverse mechanisms that still challenge our theoretical and modelling efforts. Amongst them, two are known to play a central role in shaping the networks evolution, namely the heterogeneous propensity of individuals to i) be socially active and ii) establish a new social relationships with their alters. Here, we empirically characterise these two mechanisms in seven real networks describing temporal human interactions in three different settings: scientific collaborations, Twitter mentions, and mobile phone calls. We find that the individuals’ social activity and their strategy in choosing ties where to allocate their social interactions can be quantitatively described and encoded in a simple stochastic network modelling framework. The Master Equation of the model can be solved in the asymptotic limit. The analytical solutions provide an explicit description of both the system dynamic and the dynamical scaling laws characterising crucial aspects about the evolution of the networks. The analytical predictions match with accuracy the empirical observations, thus validating the theoretical approach. Our results provide a rigorous dynamical system framework that can be extended to include other processes shaping social dynamics and to generate data driven predictions for the asymptotic behaviour of social networks
Concentration-Dependent, Size-Independent Toxicity of Citrate Capped AuNPs in Drosophila melanogaster
The expected potential benefits promised by nanotechnology in various fields have led to a rapid increase of the presence of engineered nanomaterials in a high number of commercial goods. This is generating increasing questions about possible risks for human health and environment, due to the lack of an in-depth assessment of the physical/chemical factors responsible for their toxic effects. In this work, we evaluated the toxicity of monodisperse citrate-capped gold nanoparticles (AuNPs) of different sizes (5, 15, 40, and 80 nm) in the model organism Drosophila melanogaster, upon ingestion. To properly evaluate and distinguish the possible dose- and/or size-dependent toxicity of the AuNPs, we performed a thorough assessment of their biological effects, using two different dose-metrics. In the first approach, we kept constant the total surface area of the differently sized AuNPs (Total Exposed Surface area approach, TES), while, in the second approach, we used the same number concentration of the four different sizes of AuNPs (Total Number of Nanoparticles approach, TNN). We observed a significant AuNPs-induced toxicity in vivo, namely a strong reduction of Drosophila lifespan and fertility performance, presence of DNA fragmentation, as well as a significant modification in the expression levels of genes involved in stress responses, DNA damage recognition and apoptosis pathway. Interestingly, we found that, within the investigated experimental conditions, the toxic effects in the exposed organisms were directly related to the concentration of the AuNPs administered, irrespective of their size
- …