420 research outputs found
Effect of assortative mixing in the second-order Kuramoto model
In this paper we analyze the second-order Kuramoto model presenting a
positive correlation between the heterogeneity of the connections and the
natural frequencies in scale-free networks. We numerically show that
discontinuous transitions emerge not just in disassortative but also in
assortative networks, in contrast with the first-order model. We also find that
the effect of assortativity on network synchronization can be compensated by
adjusting the phase damping. Our results show that it is possible to control
collective behavior of damped Kuramoto oscillators by tuning the network
structure or by adjusting the dissipation related to the phases movement.Comment: 7 pages, 6 figures. In press in Physical Review
Building Damage-Resilient Dominating Sets in Complex Networks against Random and Targeted Attacks
We study the vulnerability of dominating sets against random and targeted
node removals in complex networks. While small, cost-efficient dominating sets
play a significant role in controllability and observability of these networks,
a fixed and intact network structure is always implicitly assumed. We find that
cost-efficiency of dominating sets optimized for small size alone comes at a
price of being vulnerable to damage; domination in the remaining network can be
severely disrupted, even if a small fraction of dominator nodes are lost. We
develop two new methods for finding flexible dominating sets, allowing either
adjustable overall resilience, or dominating set size, while maximizing the
dominated fraction of the remaining network after the attack. We analyze the
efficiency of each method on synthetic scale-free networks, as well as real
complex networks
Multiscale mixing patterns in networks
Assortative mixing in networks is the tendency for nodes with the same
attributes, or metadata, to link to each other. It is a property often found in
social networks manifesting as a higher tendency of links occurring between
people with the same age, race, or political belief. Quantifying the level of
assortativity or disassortativity (the preference of linking to nodes with
different attributes) can shed light on the factors involved in the formation
of links and contagion processes in complex networks. It is common practice to
measure the level of assortativity according to the assortativity coefficient,
or modularity in the case of discrete-valued metadata. This global value is the
average level of assortativity across the network and may not be a
representative statistic when mixing patterns are heterogeneous. For example, a
social network spanning the globe may exhibit local differences in mixing
patterns as a consequence of differences in cultural norms. Here, we introduce
an approach to localise this global measure so that we can describe the
assortativity, across multiple scales, at the node level. Consequently we are
able to capture and qualitatively evaluate the distribution of mixing patterns
in the network. We find that for many real-world networks the distribution of
assortativity is skewed, overdispersed and multimodal. Our method provides a
clearer lens through which we can more closely examine mixing patterns in
networks.Comment: 11 pages, 7 figure
Tuning the average path length of complex networks and its influence to the emergent dynamics of the majority-rule model
We show how appropriate rewiring with the aid of Metropolis Monte Carlo
computational experiments can be exploited to create network topologies
possessing prescribed values of the average path length (APL) while keeping the
same connectivity degree and clustering coefficient distributions. Using the
proposed rewiring rules we illustrate how the emergent dynamics of the
celebrated majority-rule model are shaped by the distinct impact of the APL
attesting the need for developing efficient algorithms for tuning such network
characteristics.Comment: 10 figure
Social Network Mixing Patterns in Mergers and Acquisitions-A Simulation Experiment
In the contemporary world of global business and continuously growing competition, organizations tend to use mergers and acquisitions to enforce their position on the market. The future organizationâs design is a critical success factor in such undertakings. The field of social network analysis can enhance our uderstanding of these processes as it lets us reason about the development of networks, regardless of their origin. The analysis of mixing patterns is particularly useful as it provides an insight into how nodes in a network connect with each other. We hypothesize that organizational networks with compatible mixing patterns will be integrated more successfully. After conducting a simulation experiment, we suggest an integration model based on the analysis of network assortativity. The model can be a guideline for organizational integration, such as occurs in mergers and acquisitions.mergers & acquisition, social network,analysis, mixing patterns, assortativity, organizational design
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HIV transmission networks among transgender women in Los Angeles County, CA, USA: a phylogenetic analysis of surveillance data.
BackgroundTransgender women are among the groups at highest risk for HIV infection, with a prevalence of 27·7% in the USA; and despite this known high risk, undiagnosed infection is common in this population. We set out to identify transgender women and their partners in a molecular transmission network to prioritise public health activities.MethodsSince 2006, HIV protease and reverse transcriptase gene (pol) sequences from drug resistance testing have been reported to the Los Angeles County Department of Public Health and linked to demographic data, gender, and HIV transmission risk factor data for each case in the enhanced HIV/AIDS Reporting System. We reconstructed a molecular transmission network by use of HIV-TRAnsmission Cluster Engine (with a pairwise genetic distance threshold of 0·015 substitutions per site) from the earliest pol sequences from 22â398 unique individuals, including 412 (2%) self-identified transgender women. We examined the possible predictors of clustering with multivariate logistic regression. We characterised the genetically linked partners of transgender women and calculated assortativity (the tendency for people to link to other people with the same attributes) for each transmission risk group.Findings8133 (36·3%) of 22â398 individuals clustered in the network across 1722 molecular transmission clusters. Transgender women who indicated a sexual risk factor clustered at the highest frequency in the network, with 147 (43%) of 345 being linked to at least one other person (adjusted odds ratio [aOR] 2·0, p=0·0002). Transgender women were assortative in the network (assortativity 0·06, p<0·001), indicating that they tended to link to other transgender women. Transgender women were more likely than expected to link to other transgender women (OR 4·65, p<0·001) and cisgender men who did not identify as men who have sex with men (MSM; OR 1·53, p<0·001). Transgender women were less likely than expected to link to MSM (OR 0·75, p<0·001), despite the high prevalence of HIV among MSM. Transgender women were distributed across 126 clusters, and cisgender individuals linked to one transgender woman were 9·2 times more likely to link to a second transgender woman than other individuals in the surveillance database. Reconstruction of the transmission network is limited by sample availability, but sequences were available for more than 40% of diagnoses.InterpretationClustering of transgender women and the observed tendency for linkage with cisgender men who did not identify as MSM, shows the potential to use molecular epidemiology both to identify clusters that are likely to include undiagnosed transgender women with HIV and to improve the targeting of public health prevention and treatment services to transgender women.FundingCalifornia HIV and AIDS Research Program and National Institutes of Health-National Institute of Allergy and Infectious Diseases
Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes
In a randomized study, leveraging covariates related to the outcome (e.g.
disease status) may produce less variable estimates of the effect of exposure.
For contagion processes operating on a contact network, transmission can only
occur through ties that connect affected and unaffected individuals; the
outcome of such a process is known to depend intimately on the structure of the
network. In this paper, we investigate the use of contact network features as
efficiency covariates in exposure effect estimation. Using augmented
generalized estimating equations (GEE), we estimate how gains in efficiency
depend on the network structure and spread of the contagious agent or behavior.
We apply this approach to simulated randomized trials using a stochastic
compartmental contagion model on a collection of model-based contact networks
and compare the bias, power, and variance of the estimated exposure effects
using an assortment of network covariate adjustment strategies. We also
demonstrate the use of network-augmented GEEs on a clustered randomized trial
evaluating the effects of wastewater monitoring on COVID-19 cases in
residential buildings at the the University of California San Diego.Comment: Substantial revisio
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