Network models for ecosystem-based fishery analysis: a review of concepts and application to the Gulf of Alaska marine food web

2008 ◽  
Vol 65 (9) ◽  
pp. 1965-1982 ◽  
Author(s):  
Sarah K. Gaichas ◽  
Robert C. Francis
Keyword(s):  
Food Web ◽  
Fishery Management ◽  
Marine Ecosystem ◽  
Stomach Contents ◽  
Network Models ◽  
Small World ◽  
Gulf Of Alaska ◽  
Quantitative Information ◽  
Scale Free ◽  
Structural Impacts

We apply graph theory and network analysis to the food web of the Gulf of Alaska marine ecosystem to classify its structural properties, which suggest how the ecosystem as a whole may respond to heavy fishing pressure on its components. Three conceptual models of network structure, random, small-world, and scale-free, each have different implications for system behavior and tolerance to perturbations. We constructed two food web network models using detailed quantitative information on the stomach contents of 57 predator (fish) species collected during trawl surveys of the Gulf of Alaska between 1981 and 2002. The resulting food webs displayed both small-world and scale-free network properties, suggesting that impacts on one species might spread to many through short interaction chains and that while most food web connections are not critical, a small set of fished species support critical structural connections. Ecosystem-based fishery management should therefore first focus on protecting the highly connected species in the network to avoid structural impacts of fishing on the food web.

scholarly journals Studies on the Decrease Mechanisms of Typical Complex Networks

2021 ◽  
Author(s):  
Yuhu Qiu ◽  
Tianyang Lyu ◽  
Xizhe Zhang ◽  
Ruozhou Wang
Keyword(s):  
Average Length ◽  
Shortest Paths ◽  
Real Life ◽  
Network Models ◽  
Small World ◽  
Clustering Coefficient ◽  
Network Growth ◽  
Scale Free ◽  
Complex Network Models ◽  
High Degree

Network decrease caused by the removal of nodes is an important evolution process that is paralleled with network growth. However, many complex network models usually lacked a sound decrease mechanism. Thus, they failed to capture how to cope with decreases in real life. The paper proposed decrease mechanisms for three typical types of networks, including the ER networks, the WS small-world networks and the BA scale-free networks. The proposed mechanisms maintained their key features in continuous and independent decrease processes, such as the random connections of ER networks, the long-range connections based on nearest-coupled network of WS networks and the tendency connections and the scale-free feature of BA networks. Experimental results showed that these mechanisms also maintained other topology characteristics including the degree distribution, clustering coefficient, average length of shortest-paths and diameter during decreases. Our studies also showed that it was quite difficult to find an efficient decrease mechanism for BA networks to withstand the continuous attacks at the high-degree nodes, because of the unequal status of nodes.

COMPLEX NETWORKS: TOPOLOGY, DYNAMICS AND SYNCHRONIZATION

2002 ◽  
Vol 12 (05) ◽  
pp. 885-916 ◽  
Author(s):  
XIAO FAN WANG
Keyword(s):  
Complex Networks ◽  
Network Models ◽  
Small World ◽  
Dynamical Networks ◽  
Scale Free ◽  
Epidemic Dynamics ◽  
The Past ◽  
The Relationship

Dramatic advances in the field of complex networks have been witnessed in the past few years. This paper reviews some important results in this direction of rapidly evolving research, with emphasis on the relationship between the dynamics and the topology of complex networks. Basic quantities and typical examples of various complex networks are described; and main network models are introduced, including regular, random, small-world and scale-free models. The robustness of connectivity and the epidemic dynamics in complex networks are also evaluated. To that end, synchronization in various dynamical networks are discussed according to their regular, small-world and scale-free connections.

scholarly journals Analysis of Epileptic Seizures with Complex Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yan Ni ◽  
Yinghua Wang ◽  
Tao Yu ◽  
Xiaoli Li
Keyword(s):  
Network Models ◽  
Small World ◽  
Epileptic Seizures ◽  
Neural Model ◽  
Brain Network ◽  
Large Area ◽  
Cortical Networks ◽  
Scale Free ◽  
Multiple Channel ◽  
Functional Brain Network

Epilepsy is a disease of abnormal neural activities involving large area of brain networks. Until now the nature of functional brain network associated with epilepsy is still unclear. Recent researches indicate that the small world or scale-free attributes and the occurrence of highly clustered connection patterns could represent a general organizational principle in the human brain functional network. In this paper, we seek to find whether the small world or scale-free property of brain network is correlated with epilepsy seizure formation. A mass neural model was adopted to generate multiple channel EEG recordings based on regular, small world, random, and scale-free network models. Whether the connection patterns of cortical networks are directly associated with the epileptic seizures was investigated. The results showed that small world and scale-free cortical networks are highly correlated with the occurrence of epileptic seizures. In particular, the property of small world network is more significant during the epileptic seizures.

Network and equation-based models in epidemiology

2018 ◽  
Vol 11 (03) ◽  
pp. 1850046 ◽  
Author(s):  
Kossi Edoh ◽  
Elijah MacCarthy
Keyword(s):  
Network Models ◽  
Small World ◽  
Control Measures ◽  
Network Structures ◽  
Scale Free ◽  
Measles Outbreak ◽  
Spanish Flu ◽  
Spread Of Infection ◽  
Aggregate Population ◽  
The Impact

Network and equation-based (EB) models are two prominent methods used in the study of epidemics. While EB models use a global approach to model aggregate population, network models focus on the behavior of individuals in the population. The two approaches have been used in several areas of research, including finance, computer science, social science and epidemiology. In this study, epidemiology is used to contrast EB models with network models. The methods are based on the assumptions and properties of compartmental models. In EB models we solve a system of ordinary differential equations and in network models we simulate the spread of epidemics on contact networks using bond percolation. We examine the impact of network structures on the spread of infection by considering various networks, including Poisson, Erdős Rényi, Scale-free, and Watts–Strogatz small-world networks, and discuss how control measures can make use of the network structures. In addition, we simulate EB assumptions on Watts–Strogatz networks to determine when the results are similar to that of EB models. As a case study, we use data from the 1918 Spanish flu pandemic and that from measles outbreak to validate our results.

EXPLORING THE VULNERABILITY OF FRACTAL COMPLEX NETWORKS THROUGH CONNECTION PATTERN AND FRACTAL DIMENSION

Fractals ◽  
2019 ◽  
Vol 27 (06) ◽  
pp. 1950102
Author(s):  
DONG-YAN LI ◽  
XING-YUAN WANG ◽  
PENG-HE HUANG
Keyword(s):  
Fractal Dimension ◽  
Quantitative Research ◽  
Fractal Dimensions ◽  
Network Models ◽  
Small World ◽  
Brain Network ◽  
Complex Model ◽  
Scale Free ◽  
Connection Pattern ◽  
The Stability

The structure of network has a significant impact on the stability of the network. It is useful to reveal the effect of fractal structure on the vulnerability of complex network since it is a ubiquitous feature in many real-world networks. There have been many studies on the stability of the small world and scale-free models, but little has been down on the quantitative research on fractal models. In this paper, the vulnerability was studied from two perspectives: the connection pattern between hubs and the fractal dimensions of the networks. First, statistics expression of inter-connections between any two hubs was defined and used to represent the connection pattern of the whole network. Our experimental results show that statistic values of inter-connections were obvious differences for each kind of complex model, and the more inter-connections, the more stable the network was. Secondly, the fractal dimension was considered to be a key factor related to vulnerability. Here we found the quantitative power function relationship between vulnerability and fractal dimension and gave the explicit mathematical formula. The results are helpful to build stable artificial network models through the analysis and comparison of the real brain network.

scholarly journals Resilience of Interbank Market Networks to Shocks

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Shouwei Li ◽  
Jianmin He
Keyword(s):  
Network Models ◽  
Small World ◽  
Interbank Market ◽  
Small World Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Tiered Networks ◽  
The Stability ◽  
Random Shocks ◽  
Market Networks

This paper first constructs a tiered network model of the interbank market. Then, from the perspective of contagion risk, it studies numerically the resilience of four types of interbank market network models to shocks, namely, tiered networks, random networks, small-world networks, and scale-free networks. This paper studies the interbank market with homogeneous and heterogeneous banks and analyzes random shocks and selective shocks. The study reveals that tiered interbank market networks and random interbank market networks are basically more vulnerable against selective shocks, while small-world interbank market networks and scale-free interbank market networks are generally more vulnerable against random shocks. Besides, the results indicate that, in the four types of interbank market networks, scale-free networks have the highest stability against shocks, while small-world networks are the most vulnerable. When banks are homogeneous, faced with selective shocks, the stability of the tiered interbank market networks is slightly lower than that of random interbank market networks, whereas, in other cases, the stability of the tiered interbank market networks is basically between that of random interbank market networks and that of scale-free interbank market networks.

scholarly journals Small-World and Scale-Free Network Models for IoT Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Insoo Sohn
Keyword(s):  
Graph Theory ◽  
Complex Network ◽  
Network Topology ◽  
Network Models ◽  
Small World ◽  
Small World Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Massive Number ◽  
Complex Network Models

It is expected that Internet of Things (IoT) revolution will enable new solutions and business for consumers and entrepreneurs by connecting billions of physical world devices with varying capabilities. However, for successful realization of IoT, challenges such as heterogeneous connectivity, ubiquitous coverage, reduced network and device complexity, enhanced power savings, and enhanced resource management have to be solved. All these challenges are heavily impacted by the IoT network topology supported by massive number of connected devices. Small-world networks and scale-free networks are important complex network models with massive number of nodes and have been actively used to study the network topology of brain networks, social networks, and wireless networks. These models, also, have been applied to IoT networks to enhance synchronization, error tolerance, and more. However, due to interdisciplinary nature of the network science, with heavy emphasis on graph theory, it is not easy to study the various tools provided by complex network models. Therefore, in this paper, we attempt to introduce basic concepts of graph theory, including small-world networks and scale-free networks, and provide system models that can be easily implemented to be used as a powerful tool in solving various research problems related to IoT.

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