Using complex network metrics to predict the persistence of metapopulations with asymmetric connectivity patterns

2008 ◽  
Vol 214 (2-4) ◽  
pp. 201-209 ◽  
Author(s):  
Michael Bode ◽  
Kevin Burrage ◽  
Hugh P. Possingham
Keyword(s):  
Complex Network ◽  
Connectivity Patterns ◽  
Network Metrics

scholarly journals Quantitative method for resilience assessment framework of airport network during COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260940
Author(s):  
Jiuxia Guo ◽  
Yang Li ◽  
Zongxin Yang ◽  
Xinping Zhu
Keyword(s):  
Complex Network ◽  
Small World ◽  
Global Scale ◽  
Assessment Framework ◽  
Small World Networks ◽  
Scale Free ◽  
Global Pandemic ◽  
Network Metrics ◽  
Resilience Assessment

The resilience and vulnerability of airport networks are significant challenges during the COVID-19 global pandemic. Previous studies considered node failure of networks under natural disasters and extreme weather. Herein, we propose a complex network methodology combined with data-driven to assess the resilience of airport networks toward global-scale disturbance using the Chinese airport network (CAN) and the European airport network (EAN) as a case study. The assessment framework includes vulnerability and resilience analyses from the network- and node-level perspectives. Subsequently, we apply the framework to analyze the airport networks in China and Europe. Specifically, real air traffic data for 232 airports in China and 82 airports in Europe are selected to form the CAN and EAN, respectively. The complex network analysis reveals that the CAN and the EAN are scale-free small-world networks, that are resilient to random attacks. However, the connectivity and vulnerability of the CAN are inferior to those of the EAN. In addition, we select the passenger throughput from the top-50 airports in China and Europe to perform a comparative analysis. By comparing the resilience evaluation of individual airports, we discovered that the factors of resilience assessment of an airport network for global disturbance considers the network metrics and the effect of government policy in actual operations. Additionally, this study also proves that a country’s emergency response-ability towards the COVID-19 has a significantly affectes the recovery of its airport network.

Characterization and Coarsening of Autonomous System Networks

2016 ◽  
pp. 148-179 ◽  
Author(s):  
Alberto Garcia-Robledo ◽  
Arturo Diaz-Perez ◽  
Guillermo Morales-Luna
Keyword(s):  
Experimental Study ◽  
Complex Network ◽  
Autonomous System ◽  
The Internet ◽  
Linear Relationships ◽  
Network Properties ◽  
Network Metrics ◽  
Graph Coarsening

This Chapter studies the correlations among well-known complex network metrics and presents techniques to coarse the topology of the Internet at the Autonomous System (AS) level. We present an experimental study on the linear relationships between a rich set of complex network metrics, to methodologically select a subset of non-redundant and potentially independent metrics that explain different aspects of the topology of the Internet. Then, the selected metrics are used to evaluate graph coarsening algorithms to reduce the topology of AS networks. The presented coarsening algorithms exploit the k-core decomposition of graphs to preserve relevant complex network properties.

scholarly journals Neural Network Embedding of Functional Microconnectome.

2022 ◽  
Author(s):  
Arata Shirakami ◽  
Takeshi Hase ◽  
Yuki Yamaguchi ◽  
Masanori Shimono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Complex Network ◽  
Second Step ◽  
Network System ◽  
Network Embedding ◽  
Neural Network System ◽  
Network Patterns ◽  
Network Metrics ◽  
Electrical Activities

Abstract Our brain works as a vast and complex network system. We need to compress the networks to extract simple principles of network patterns and interpret these paradigms to better comprehend their complexities. This study treats this simplification process using a two-step analysis of topological patterns of functional connectivities that were produced from electrical activities of ~1000 neurons from acute slices of mouse brains [Kajiwara et al. 2021] As the first step, we trained an artificial neural network system called neural network embedding (NNE) and automatically compressed the functional connectivities. As the second step, we widely compared the compressed features with 15 representative network metrics, having clear interpretations, including not only common metrics, such as centralities clusters and modules but also newly developed network metrics. The result demonstrates not only the fact that the newly developed network metrics could complementarily explain the features of what was compressed by the NNE method but was previously relatively hard to explain using common metrics such as hubs, clusters and communities. This NNE method surpasses the limitations of commonly used human-made metrics but also provides the possibility that recognizing our own limitations drives us to extend interpretable targets by developing new network metrics.

A descriptive study of fracture networks in rocks using complex network metrics

2016 ◽  
Vol 88 ◽  
pp. 97-114 ◽  
Author(s):  
Elizabeth Santiago ◽  
Jorge X. Velasco-Hernández ◽  
Manuel Romero-Salcedo
Keyword(s):  
Complex Network ◽  
Descriptive Study ◽  
Fracture Networks ◽  
Network Metrics

scholarly journals The brain as a complex network: assessment of EEG-based functional connectivity patterns in patients with childhood absence epilepsy

2020 ◽  
Vol 22 (5) ◽  
pp. 519-530
Author(s):  
Eva Paradiž Leitgeb ◽  
Marko Šterk ◽  
Timotej Petrijan ◽  
Peter Gradišnik ◽  
Marko Gosak
Keyword(s):  
Functional Connectivity ◽  
Complex Network ◽  
Absence Epilepsy ◽  
Childhood Absence Epilepsy ◽  
Connectivity Patterns ◽  
The Brain

scholarly journals Indirectly connected: simple social differences can explain the causes and apparent consequences of complex social network positions

2017 ◽  
Vol 284 (1867) ◽  
pp. 20171939 ◽  
Author(s):  
Josh A. Firth ◽  
Ben C. Sheldon ◽  
Lauren J. N. Brent
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Complex Network ◽  
Explanatory Power ◽  
Sufficient Evidence ◽  
Social Differences ◽  
Social Decisions ◽  
Direct Network ◽  
Network Metrics ◽  
Animal Social Networks

Animal societies are often structurally complex. How individuals are positioned within the wider social network (i.e. their indirect social connections) has been shown to be repeatable, heritable and related to key life-history variables. Yet, there remains a general lack of understanding surrounding how complex network positions arise, whether they indicate active multifaceted social decisions by individuals, and how natural selection could act on this variation. We use simulations to assess how variation in simple social association rules between individuals can determine their positions within emerging social networks. Our results show that metrics of individuals' indirect connections can be more strongly related to underlying simple social differences than metrics of their dyadic connections. External influences causing network noise (typical of animal social networks) generally inflated these differences. The findings demonstrate that relationships between complex network positions and other behaviours or fitness components do not provide sufficient evidence for the presence, or importance, of complex social behaviours, even if direct network metrics provide less explanatory power than indirect ones. Interestingly however, a plausible and straightforward heritable basis for complex network positions can arise from simple social differences, which in turn creates potential for selection to act on indirect connections.

scholarly journals Complex network models reveal correlations among network metrics, exercise intensity and role of body changes in the fatigue process

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Vanessa Helena Pereira ◽  
Maria Carolina Traina Gama ◽  
Filipe Antônio Barros Sousa ◽  
Theodore Gyle Lewis ◽  
Claudio Alexandre Gobatto ◽  
...  
Keyword(s):  
Complex Network ◽  
Exercise Intensity ◽  
Network Models ◽  
Fatigue Process ◽  
Complex Network Models ◽  
Network Metrics
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