scholarly journals Foreshocks and Short-Term Hazard Assessment to Large Earthquakes using Complex Networks: the Case of the 2009 L'Aquila Earthquake

2016 ◽  
Author(s):  
E. Daskalaki ◽  
K. Spiliotis ◽  
C. Siettos ◽  
G. Minadakis ◽  
G. A. Papadopoulos
Keyword(s):  
Hazard Assessment ◽  
Betweenness Centrality ◽  
Small World ◽  
Clustering Coefficient ◽  
Event Space ◽  
Short Term ◽  
2009 L’Aquila Earthquake ◽  
Topological Measures ◽  
Network Properties ◽  
Large Earthquakes

Abstract. The monitoring of statistical network properties could be useful for the short-term hazard assessment of the occurrence of mainshocks in the presence of foreshocks. Using successive connections between events acquired from the earthquake catalogue of INGV for the case of the L'Aquila (Italy) mainshock (Mw = 6.3) of 6th April 2009, we provide evidence that network measures, both global (e.g. average clustering coefficient, small-world index) and local (betweenness centrality), could potentially be exploited for forecasting purposes both in time and space. Our results reveal statistically significant increases of the topological measures and a nucleation of the betweenness centrality around the location of the epicenter about two months before the mainshock. The results of the analysis are robust even when considering either large or off-centered the main event space-windows.

scholarly journals Foreshocks and short-term hazard assessment of large earthquakes using complex networks: the case of the 2009 L'Aquila earthquake

2016 ◽  
Vol 23 (4) ◽  
pp. 241-256 ◽  
Author(s):  
Eleni Daskalaki ◽  
Konstantinos Spiliotis ◽  
Constantinos Siettos ◽  
Georgios Minadakis ◽  
Gerassimos A. Papadopoulos
Keyword(s):  
Hazard Assessment ◽  
Betweenness Centrality ◽  
Small World ◽  
Clustering Coefficient ◽  
Earthquake Catalog ◽  
Short Term ◽  
2009 L’Aquila Earthquake ◽  
Topological Measures ◽  
Network Properties ◽  
Large Earthquakes

Abstract. The monitoring of statistical network properties could be useful for the short-term hazard assessment of the occurrence of mainshocks in the presence of foreshocks. Using successive connections between events acquired from the earthquake catalog of the Istituto Nazionale di Geofisica e Vulcanologia (INGV) for the case of the L'Aquila (Italy) mainshock (Mw = 6.3) of 6 April 2009, we provide evidence that network measures, both global (average clustering coefficient, small-world index) and local (betweenness centrality) ones, could potentially be exploited for forecasting purposes both in time and space. Our results reveal statistically significant increases in the topological measures and a nucleation of the betweenness centrality around the location of the epicenter about 2 months before the mainshock. The results of the analysis are robust even when considering either large or off-centered the main event space windows.

Abstract 17426: Pulmonary Vein Isolation ‘Rewires’ Electrical Communications to Enhance Small-world Network Topology During Atrial Fibrillation

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Hiroshi Ashikaga ◽  
Jonathan Chrispin ◽  
Degang Wu ◽  
Joshua Garland
Keyword(s):  
Atrial Fibrillation ◽  
Communication Network ◽  
Mutual Information ◽  
Path Length ◽  
Random Network ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Network ◽  
Z Score ◽  
Network Properties

Recent evidence suggests that pulmonary vein isolation (PVI) may perturb the electrophysiological substrate for maintenance of atrial fibrillation (AF). Our previous work indicates that information theory metrics can quantify electrical communications during arrhythmia. We hypothesized that PVI ‘rewires’ the electrical communication network during AF such that the topology exhibits higher levels of small-world network properties, with higher clustering coefficient and lower path length, than would be expected by chance. Thirteen consecutive patients (n=6 with prior PVI and n=7 without) underwent AF ablation using a 64-electrode basket catheter in the left atrium. Multielectrode recording was performed during AF for 60 seconds, followed by PVI. Mutual information was calculated from the time series between each pair of electrodes using the Kraskov-Stögbauer-Grassberger estimator. The all-to-all mutual information matrix (64x64; Figure, upper panels) was thresholded by the median and standard deviations of mutual information to build a binary adjacency matrix for electrical communication networks. The properties of small-world network ( swn ; ‘small-world-ness’) were quantified by the ratio of the observed average clustering coefficient to that of a random network over the ratio of the observed average path length to that of a random network. swn was expressed in normal Z standard deviation units. As the binarizing threshold increased, the Z-score of swn decreased (Figure, lower panel). However, the Z-score at each threshold value was consistently higher with prior PVI than those without (p<0.05). In conclusion, electrical communication network during AF with prior PVI is associated with higher levels of small-world network properties than those without. This finding supports the concept that PVI perturbs the underlying substrate. In addition, swn of electrical communication network may be a promising metric to quantify substrate modification.

OPTIMAL INTERCONNECTION OF AD HOC INJECTION NETWORKS

2008 ◽  
Vol 09 (03) ◽  
pp. 277-297 ◽  
Author(s):  
GREGOIRE DANOY ◽  
ENRIQUE ALBA ◽  
PASCAL BOUVRY
Keyword(s):  
Ad Hoc Networks ◽  
Ad Hoc ◽  
Small World ◽  
Access Point ◽  
Clustering Coefficient ◽  
Mobility Patterns ◽  
Characteristic Path Length ◽  
Network Properties ◽  
Hoc Networks ◽  
Topology Control Problem

Multi-hop ad hoc networks allow establishing local groups of communicating devices in a self-organizing way. However, when considering realistic mobility patterns, such networks most often get divided in a set of disjoint partitions. This presence of partitions is an obstacle to communication within these networks. Ad hoc networks are generally composed of devices capable of communicating in a geographical neighborhood for free (e.g. using Wi-Fi or Bluetooth). In most cases a communication infrastructure is available. It can be a set of access point as well as a GSM/UMTS network. The use of such an infrastructure is billed, but it permits to interconnect distant nodes, through what we call “bypass links”. The objective of our work is to optimize the placement of these long-range links. To this end we rely on small-world network properties, which consist in a high clustering coefficient and a low characteristic path length. In this article we investigate the use of three genetic algorithms (generational, steady-state, and cooperative coevolutionary) to optimize three instances of this topology control problem and present initial evidence of their capacity to solve it.

scholarly journals Robustness in Weighted Networks with Cluster Structure

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yi Zheng ◽  
Fang Liu ◽  
Yong-Wang Gong
Keyword(s):  
Betweenness Centrality ◽  
Cluster Structure ◽  
Small World ◽  
Weighting Scheme ◽  
Clustering Coefficient ◽  
Fixed Degree ◽  
Scale Free ◽  
Optimal Region ◽  
Weighting Strategy ◽  
The One

The vulnerability of complex systems induced by cascade failures revealed the comprehensive interaction of dynamics with network structure. The effect on cascade failures induced by cluster structure was investigated on three networks, small-world, scale-free, and module networks, of which the clustering coefficient is controllable by the random walk method. After analyzing the shifting process of load, we found that the betweenness centrality and the cluster structure play an important role in cascading model. Focusing on this point, properties of cascading failures were studied on model networks with adjustable clustering coefficient and fixed degree distribution. In the proposed weighting strategy, the path length of an edge is designed as the product of the clustering coefficient of its end nodes, and then the modified betweenness centrality of the edge is calculated and applied in cascade model as its weights. The optimal region of the weighting scheme and the size of the survival components were investigated by simulating the edge removing attack, under the rule of local redistribution based on edge weights. We found that the weighting scheme based on the modified betweenness centrality makes all three networks have better robustness against edge attack than the one based on the original betweenness centrality.

scholarly journals Cross-Shareholdings Structural Characteristic and Evolution Analysis Based on Complex Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaohong Chang ◽  
Haiyun Wang
Keyword(s):  
Complex Network ◽  
Network Structure ◽  
Practical Importance ◽  
Small World ◽  
Clustering Coefficient ◽  
Policy Makers ◽  
Scale Free ◽  
Network Properties ◽  
The Cross ◽  
Cross Shareholdings

This study depicts the network morphology of firms which establish ties through cross-shareholdings by the theory of complex network analysis method. It calculates some complex network properties of the cross-shareholdings network and analyzes the evolution law of network structure in nearly 7 years. The network clearly displays small world properties and scale-free properties. The cross-shareholdings network average path length and clustering coefficient is with a small amplitude fluctuation; the network structure is relatively stable. Such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the cross-shareholdings network.

COMPARATIVE ANALYSIS OF METABOLIC NETWORKS IN MESOPHILIC AND THERMOPHILIC ARCHAEA METHANOGENS BASED ON MODULARITY

2013 ◽  
Vol 21 (02) ◽  
pp. 1350015 ◽  
Author(s):  
JING CHEN ◽  
YANRUI DING ◽  
WENBO XU
Keyword(s):  
Metabolic Networks ◽  
Small World ◽  
Clustering Coefficient ◽  
Modular Organization ◽  
Methanosarcina Acetivorans ◽  
Characteristic Path Length ◽  
Metabolic Functions ◽  
Thermophilic Archaeon ◽  
Network Properties ◽  
Methanopyrus Kandleri

Metabolic networks are useful representations of the metabolic capabilities of cells. A comparison of metabolic networks across species is essential to better understand how evolutionary pressures shape these networks. By comparing the set of reactions that are expected to occur in an organism with the set of reactions in reference metabolic pathways, it is possible to infer the main metabolic functions of an organism. In this paper, the metabolic networks of the mesophilic archaeon Methanosarcina acetivorans and the thermophilic archaeon Methanopyrus kandleri have been reconstructed based on the KEGG LIGAND database, followed by four topological statistical analyses of the nodes in the two networks to compare their metabolic networks. The values of average degree and characteristic path length are very small but clustering coefficient is relatively large. The results show that the complete metabolic networks of M. acetivorans and M. kandleri possessed "small-world" network properties. Then we used Girvan–Newman modular algorithm to identify hub modules and compared hub modules with non-hub modules, respectively. The results show that M. kandleri metabolic network has a better modular organization than the M. acetivorans network. M. acetivorans includes 39 modules, 25 modules of them are independent, and 15 modules are functionally pure. On the other hand, M. kandleri includes 30 modules. Among them, there are 20 independent modules, and 14 of them are functionally pure. These results further indicated that the present approach for identifying modules yields modules that have biologically significant functions. We also identified hub modules of the metabolic networks and found that these hub modules are carbohydrate metabolism and amino acid metabolism. The conclusions obtained from such studies provide a broad overview of the similarities and differences between organism's metabolic networks. These will be very helpful for further research on thermostability of methanogens.

Network-based approach to explore basin network comparing observed and satellite dataset

2021 ◽  
Author(s):  
Mayuri Gadhawe ◽  
Ravi Kumar Guntu ◽  
Ankit Agarwal
Keyword(s):  
Correlation Coefficient ◽  
Random Network ◽  
Pearson Correlation ◽  
Correlation Method ◽  
Small World ◽  
Clustering Coefficient ◽  
Node Degree ◽  
Network Measures ◽  
Network Properties ◽  
Precipitation Network

&lt;p&gt;Complex network is a relatively young, multidisciplinary field with an objective to unravel the spatiotemporal interaction in natural processes. Though network theory has become a very important paradigm in many fields, the applications in the hydrology field are still at an emerging stage.&amp;#160; In this study, we employed the Pearson correlation coefficient and Spearman correlation coefficient as a similarity measure with varying threshold ranges to construct the precipitation network of the Ganga River Basin (GRB). Ground-based observed dataset (IMD) and satellite precipitation product (TRMM) are used. Different network properties such as node degree, degree distribution, clustering coefficient, and architecture were computed on each resultant precipitation network of GRB. We also ranked influential grid points in the precipitation network by using weighted degree betweenness to identify the importance of each grid station in the network Our results reveal that the choice of correlation method does not significantly affect the network measures and reconfirm that the thresholds significantly influence network construction and network properties in the case of both datasets. The spatial distribution of the clustering coefficient value is high to low from center to boundary and inverse in the case of degree.&amp;#160; In addition, there is a positive correlation between the average neighbor degree and node degree. Again, we analyzed the architecture of precipitation networks and found that the network has a small world with random network behavior. &amp;#160;&amp;#160;Our results also indicated that both products have similar network measures and showed similar kinds of spatial patterns.&lt;/p&gt;

scholarly journals Properties of latent variable network models

2016 ◽  
Vol 4 (4) ◽  
pp. 407-432 ◽  
Author(s):  
RICCARDO RASTELLI ◽  
NIAL FRIEL ◽  
ADRIAN E. RAFTERY
Keyword(s):  
Latent Variable ◽  
Broad Class ◽  
Network Models ◽  
Small World ◽  
Clustering Coefficient ◽  
Analytic Expressions ◽  
Network Properties ◽  
Degree Correlations ◽  
Heavy Tailed ◽  
Special Case

AbstractWe derive properties of latent variable models for networks, a broad class of models that includes the widely used latent position models. We characterize several features of interest, with particular focus on the degree distribution, clustering coefficient, average path length, and degree correlations. We introduce the Gaussian latent position model, and derive analytic expressions and asymptotic approximations for its network properties. We pay particular attention to one special case, the Gaussian latent position model with random effects, and show that it can represent the heavy-tailed degree distributions, positive asymptotic clustering coefficients, and small-world behaviors that often occur in observed social networks. Finally, we illustrate the ability of the models to capture important features of real networks through several well-known datasets.

CELLULAR NEURAL NETWORKS CAN MIMIC SMALL-WORLD NETWORKS

1999 ◽  
Vol 09 (10) ◽  
pp. 2105-2126 ◽  
Author(s):  
TAO YANG ◽  
LEON O. CHUA
Keyword(s):  
Neural Networks ◽  
Coupling Strength ◽  
Initial Conditions ◽  
Computing Time ◽  
Small World ◽  
Cellular Neural Networks ◽  
Clustering Coefficient ◽  
High Survival ◽  
Game Of Life ◽  
Hole Detection

Small-world phenomenon can occur in coupled dynamical systems which are highly clustered at a local level and yet strongly coupled at the global level. We show that cellular neural networks (CNN's) can exhibit "small-world phenomenon". We generalize the "characteristic path length" from previous works on "small-world phenomenon" into a "characteristic coupling strength" for measuring the average coupling strength of the outputs of CNN's. We also provide a simplified algorithm for calculating the "characteristic coupling strength" with a reasonable amount of computing time. We define a "clustering coefficient" and show how it can be calculated by a horizontal "hole detection" CNN, followed by a vertical "hole detection" CNN. Evolutions of the game-of-life CNN with different initial conditions are used to illustrate the emergence of a "small-world phenomenon". Our results show that the well-known game-of-life CNN is not a small-world network. However, generalized CNN life games whose individuals have strong mobility and high survival rate can exhibit small-world phenomenon in a robust way. Our simulations confirm the conjecture that a population with a strong mobility is more likely to qualify as a small world. CNN games whose individuals have weak mobility can also exhibit a small-world phenomenon under a proper choice of initial conditions. However, the resulting small worlds depend strongly on the initial conditions, and are therefore not robust.

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