Community Detection in Dynamic Networks via Adaptive Label Propagation

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.Comment: 16 pages, 11 figure

Multifractal and Network Analysis of Phase Transition

Many models and real complex systems possess critical thresholds at which the systems shift from one sate to another. The discovery of the early warnings of the systems in the vicinity of critical point are of great importance to estimate how far a system is from a critical threshold. Multifractal Detrended Fluctuation analysis (MF-DFA) and visibility graph method have been employed to investigate the fluctuation and geometrical structures of magnetization time series of two-dimensional Ising model around critical point. The Hurst exponent has been confirmed to be a good indicator of phase transition. Increase of the multifractality of the time series have been observed from generalized Hurst exponents and singularity spectrum. Both Long-term correlation and broad probability density function are identified to be the sources of multifractality of time series near critical regime. Heterogeneous nature of the networks constructed from magnetization time series have validated the fractal properties of magnetization time series from complex network perspective. Evolution of the topology quantities such as clustering coefficient, average degree, average shortest path length, density, assortativity and heterogeneity serve as early warnings of phase transition. Those methods and results can provide new insights about analysis of phase transition problems and can be used as early warnings for various complex systems.Comment: 23 pages, 11 figure

Climate change impact on China food security in 2050

Climate change is now affecting global agriculture and food production worldwide. Nonetheless the direct link between climate change and food security at the national scale is poorly understood. Here we simulated the effect of climate change on food security in China using the CERES crop models and the IPCC SRES A2 and B2 scenarios including CO2 fertilization effect. Models took into account population size, urbanization rate, cropland area, cropping intensity and technology development. Our results predict that food crop yield will increase +3-11 % under A2 scenario and +4 % under B2 scenario during 2030-2050, despite disparities among individual crops. As a consequence China will be able to achieve a production of 572 and 615 MT in 2030, then 635 and 646 MT in 2050 under A2 and B2 scenarios, respectively. In 2030 the food security index (FSI) will drop from +24 % in 2009 to -4.5 % and +10.2 % under A2 and B2 scenarios, respectively. In 2050, however, the FSI is predicted to increase to +7.1 % and +20.0 % under A2 and B2 scenarios, respectively, but this increase will be achieved only with the projected decrease of Chinese population. We conclude that 1) the proposed food security index is a simple yet powerful tool for food security analysis; (2) yield growth rate is a much better indicator of food security than yield per se; and (3) climate change only has a moderate positive effect on food security as compared to other factors such as cropland area, population growth, socio-economic pathway and technology development. Relevant policy options and research topics are suggested accordingly

Heat shock proteins in stabilization of spontaneously restored sinus rhythm in permanent atrial fibrillation patients after mitral valve surgery

A spontaneously restored sinus rhythm in permanent atrial fibrillation patients has been often observed after mitral valve (MV) surgery, but persisting duration in sinus rhythm varies from patient to patient. Heat shock proteins (Hsps) may be involved in pathogenesis of atrial fibrillation. We hypothesized that stabilization of restored sinus rhythm is associated with expression of Hsps in the atria. To test this hypothesis, clinical data, biopsies of right atrial appendage, and blood samples were collected from 135 atrial fibrillation patients who spontaneously restored sinus rhythm after conventional isolated MV replacement. Comparison was made between patients who had recurrence of atrial fibrillation within 7 days (AF) vs. patients with persisted sinus rhythm for more than 7 days (SR). Results showed that SR patients had higher activity of heat shock transcription factor 1 (HSF1) as well as upregulated expressions of heat shock cognate 70, Hsp70, and Hsp27 in the tissues. The activation of HSF1–Hsps pathway was associated with less-aggressive pathogenesis as reflected by lower rates of myolysis, apoptosis, interstitial fibrosis, and inflammation in SR patients. However, Hsp60 was lower in both tissue and plasma in SR patients, and was positively correlated with apoptosis, interstitial fibrosis, and inflammation. These findings suggest that the Hsps play important roles in stabilization of restored sinus rhythm after MV surgery by inhibiting AF-related atrial remodeling and arrhythmogenic substrates in atrial fibrillation patients. Low circulating Hsp60 levels preoperatively might predict a stable spontaneously restored sinus rhythm postoperatively

(Color online) Results on the AS-Internet dataset.

<p>Comparison of (a) modularity and (b) consuming time of ALPA at each snapshot with FacetNet, iLCD and Infomap on the AS-Internet dataset.</p

Visibility graphs at different temperatures.

<p>(Color online) The network structures of the visibility graphs at (a) <i>T</i>/<i>T</i>_<i>c</i> = 0.52, (b) <i>T</i>/<i>T</i>_<i>c</i> = 1.00, (c) <i>T</i>/<i>T</i>_<i>c</i> = 1.59 for system size <i>N</i> = 100 × 100. The network sizes are 10,000. Different colors represent different communities.</p

Average Magnetization time series per spin.

<p>Average magnetization <i>M</i> as a function of time for the two-dimensional Ising model at different temperatures.</p

(Color online) Normalized mutual information (NMI) as a function of the mixing parameter <i>μ</i> in LFR networks.

<p>Four network scenarios are shown, which correspond to two different network sizes (<i>N</i> = 1000,5000) and, for a given size, to two different ranges for the community sizes (<i>C</i> = [10, 50], [20, 100]). Each point on the curves corresponds to the average value of the NMI value over 100 network realizations.</p

(Color online) Results of the application of different methods on the three standard benchmarks (in columns).

<p>The first row corresponds to the planted partition of each benchmark, while the three remaining rows are the partitions detected by different algorithms. In each plot, the vertical axis corresponds to the index of nodes in the network, while the horizontal axis represents the time. The color of each pair {node, time} indicates the community to which the node is belongs at that specific time.</p

Generalized Hurst exponent for shuffled time series.

<p>(Color online) Heat map of ensemble average of the generalized Hurst exponents <i>h</i>(<i>q</i>) for <i>q</i> ∈ [−5, 5] at different temperatures for the shuffled time series with different system sizes.</p