scholarly journals COMPLEX NETWORK SIMULATION OF FOREST NETWORK SPATIAL PATTERN IN PEARL RIVER DELTA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1445-1449
Author(s):  
Y. Zeng
Keyword(s):  
Power Law ◽  
Pearl River Delta ◽  
Pearl River ◽  
Network Node ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Network Nodes ◽  
Scale Free ◽  
Node Distribution ◽  
Free Network

Forest network-construction uses for the method and model with the scale-free features of complex network theory based on random graph theory and dynamic network nodes which show a power-law distribution phenomenon. The model is suitable for ecological disturbance by larger ecological landscape Pearl River Delta consistent recovery. Remote sensing and GIS spatial data are available through the latest forest patches. A standard scale-free network node distribution model calculates the area of forest network’s power-law distribution parameter value size; The recent existing forest polygons which are defined as nodes can compute the network nodes decaying index value of the network’s degree distribution. The parameters of forest network are picked up then make a spatial transition to GIS real world models. Hence the connection is automatically generated by minimizing the ecological corridor by the least cost rule between the near nodes. Based on scale-free network node distribution requirements, select the number compared with less, a huge point of aggregation as a future forest planning network’s main node, and put them with the existing node sequence comparison. By this theory, the forest ecological projects in the past avoid being fragmented, scattered disorderly phenomena. The previous regular forest networks can be reduced the required forest planting costs by this method. For ecological restoration of tropical and subtropical in south China areas, it will provide an effective method for the forest entering city project guidance and demonstration with other ecological networks (water, climate network, etc.) for networking a standard and base datum.

Scale-Free Networks Based on the Value of Interest

2013 ◽  
Vol 753-755 ◽  
pp. 2959-2962
Author(s):  
Jun Tao Yang ◽  
Hui Wen Deng
Keyword(s):  
Network Model ◽  
Power Law ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Scale Free ◽  
Distribution Property ◽  
Scale Free Networks ◽  
Free Model ◽  
Free Network ◽  
Interest Model

Assigning the value of interest to each node in the network, we give a scale-free network model. The value of interest is related to the fitness and the degree of the node. Experimental results show that the interest model not only has the characteristics of the BA scale-free model but also has the characteristics of fitness model, and the network has a power-law distribution property.

scholarly journals Evolution of Cooperation through Power Law Distributed Conflicts

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Pilwon Kim
Keyword(s):  
Power Law ◽  
Evolution Of Cooperation ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Homogeneous Population ◽  
Interpersonal Conflicts ◽  
Personal Resource ◽  
Scale Free ◽  
Individual Level ◽  
Free Network

At an individual level, cooperation can be seen as a behaviour that uses personal resource to support others or the groups which one belongs to. In a conflict between two individuals, a selfish person gains an advantage over a cooperative opponent, while in a group-group conflict the group with more cooperators wins. In this work, we develop a population model with continual conflicts at various scales and show cooperation can be sustained even when interpersonal conflicts dominate, as long as the conflict size follows a power law. The power law assumption has been met in several observations from real-world conflicts. Specifically if the population is structured on a scale-free network, both the power law distribution of conflicts and the survival of cooperation can be naturally induced without assuming a homogeneous population or frequent relocation of members. On the scale-free network, even when most people become selfish from continual person-person conflicts, people on the hubs tend to remain unselfish and play a role as “repositories” of cooperation.

scholarly journals The Formation of Social Network Assortativity: A Cultural Trait-Matching Mechanism

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Wei Wang ◽  
Xiaoming Sun ◽  
Yalan Wang ◽  
Wentian Cui
Keyword(s):  
Preferential Attachment ◽  
Scale Free Network ◽  
Selection Probability ◽  
Network Nodes ◽  
Scale Free ◽  
Cultural Trait ◽  
Free Network ◽  
Matching Mechanism ◽  
Simulation Results ◽  
Attachment Mechanism

The preferential attachment mechanism that forms scale-free network cannot display assortativity, i.e., the degree of one node is positively correlated with that of their neighbors in the network. Given the attributes of network nodes, a cultural trait-matching mechanism is further introduced in this paper. Both theoretical analysis and simulation results indicate that the higher selection probability of such mechanism, the more obvious the assortativity is shown in networks. Further, the degree of nodes presents a positive logarithm correlation with that of adjacent ones. Finally, this study discusses the theoretical and practical significances of the introduction of such a cultural trait-matching mechanism.

scholarly journals Truncation of Power Law Behavior in “Scale-Free” Network Models due to Information Filtering

2002 ◽  
Vol 88 (13) ◽  
Author(s):  
Stefano Mossa ◽  
Marc Barthélémy ◽  
H. Eugene Stanley ◽  
Luís A. Nunes Amaral
Keyword(s):  
Power Law ◽  
Information Filtering ◽  
Network Models ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

An Abnormal IP Traffic Detection Model Based on Scale-Free Network

2010 ◽  
Vol 44-47 ◽  
pp. 849-853
Author(s):  
Jun Li ◽  
Yan Niu
Keyword(s):  
Detection System ◽  
Anomalous Behavior ◽  
Power Exponent ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Scale Free ◽  
Detection Model ◽  
Free Network ◽  
Traffic Detection ◽  
Entire Network

A model of detecting an abnormal IP traffic in a subset of network is described. The model is based on the hypothesis that random sampling subnet are the same probability distribution as the entire network if some conditions are met with, nodes’s degree in IP traffic can be processed as a power-law distribution in scale-free network . The model analyzes the power exponent and relations between the anomalous behavior and parameter r. Finally, a test was conducted by the data, some type attacks could be identified exactly. the model provides a new framework for intrusion-detection system.

THE RELEVANCE-STRENGTH IN A SCALE-FREE NETWORK

2008 ◽  
Vol 22 (31) ◽  
pp. 3053-3059 ◽  
Author(s):  
HYUN-JOO KIM
Keyword(s):  
Network Model ◽  
Shortest Path ◽  
Power Law ◽  
Path Length ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
The Mean

We introduce a new quantity, relevance-strength which describes the relevance of a node to the others in a scale-free network. We define a weight between two nodes i and j based on the shortest path length between them and the relevance-strength of a node is defined as the sum of the weights between it and others. For the Barabási and Albert model which is a well-known scale-free network model, we measure the relevance-strength of each node and study the correlations with other quantities, such as the degree, the mean degree of neighbors of a node, and the mean relevance-strength of neighbors. We find that the relevance-strength shows power law behaviors and the crossover behaviors for the degree and the mean relevance-strength of neighbors. Also, we study the scaling behaviors of the relevance-strength for various average relevance-strength for all nodes.

scholarly journals Bipartite subgraphs and the signless Laplacian matrix

2011 ◽  
Vol 5 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Steve Kirkland ◽  
Debdas Paul
Keyword(s):  
Laplacian Matrix ◽  
Rayleigh Quotient ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Laplacian Eigenvalue ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Signless Laplacian Matrix ◽  
Signless Laplacian ◽  
Free Network

For a connected graph G, we derive tight inequalities relating the smallest signless Laplacian eigenvalue to the largest normalized Laplacian eigenvalue. We investigate how vectors yielding small values of the Rayleigh quotient for the signless Laplacian matrix can be used to identify bipartite subgraphs. Our results are applied to some graphs with degree sequences approximately following a power law distribution with exponent value 2:1 (scale-free networks), and to a scale-free network arising from protein-protein interaction.

AVALANCHES OF BAK–SNEPPEN COEVOLUTION MODEL ON DIRECTED SCALE-FREE NETWORK

Fractals ◽  
2009 ◽  
Vol 17 (02) ◽  
pp. 233-237 ◽  
Author(s):  
KYOUNG EUN LEE ◽  
JAE WOO LEE
Keyword(s):  
Probability Distribution ◽  
Power Law ◽  
Critical Exponents ◽  
Return Time ◽  
Scale Free Network ◽  
Avalanche Size ◽  
Scale Free ◽  
Free Network ◽  
First Return Time ◽  
Physical Quantities

We study the critical properties of the Bak–Sneppen coevolution model on scale-free networks by Monte Carlo method. We report the distribution of the avalanche size and fractal activity through the branching process. We observe that the critical fitness fc(N) depends on the number of the node such as fc(N) ~ 1/ log (N) for both the scale-free network and the directed scale-free network. Near the critical fitness many physical quantities show power-law behaviors. The probability distribution P(s) of the avalanche size at the critical fitness shows a power-law like P(s) ~ s-τ with τ = 1.53(5) regardless of the scale-free network and the directed scale free network. The probability distribution Pf(t) of the first return time also shows a power-law such as Pf(t) ~ t-τf. The critical exponents τf are equivalent for both the scale-free network and the directed scale-free network. We obtain the critical exponents as τf = 1.776(5) at the scalinge regime. The directionality of the network does not change the universality on the network.

ANALYSIS OF A COMPLEX NETWORK OF PHYSICS CONCEPTS

2012 ◽  
Vol 26 (28) ◽  
pp. 1250186 ◽  
Author(s):  
HYUN-JOO KIM
Keyword(s):  
Power Law ◽  
Betweenness Centrality ◽  
Random Network ◽  
The Other ◽  
Topological Properties ◽  
Scale Free Network ◽  
Scale Free ◽  
Energy Concept ◽  
Physics Concepts ◽  
Free Network

We construct the physics concepts network in which the physics concepts is considered as nodes. We find that this network is clearly not a random network but a scale-free network in which the distribution P(k) of the degree k follows a power law, P(k) ~ k-γ with exponent γ ≈ 2.41. The relevance between the physics concepts and the important concepts are studied by measuring the degree, the betweenness centrality, and the relevance strength and we find that the energy concept is most important. Also we find that the relevance strength s(k) as a function of the degree k exhibits a power-law behavior, s(k) ~ kα with the exponent α ≈ 0.15 and s(knn) as a function of the average neighbor's degree knn follows a power-law behavior, [Formula: see text] with the exponent δ ≈ 0.17 for small knn. We also measured the other quantities which describe the topological properties of the network and find that it has the hierarchical property and tree-like patterns.

scholarly journals Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank

2020 ◽  
Vol 10 (23) ◽  
pp. 8349 ◽  
Author(s):  
Wei-Chang Yeh ◽  
Chia-Chen Kuo
Keyword(s):  
Global Warming ◽  
Environmental Impacts ◽  
Power Law ◽  
The State ◽  
Wildland Fires ◽  
Scale Free Network ◽  
Scale Free ◽  
Reliable Tool ◽  
Free Network ◽  
Binary Addition

The nature and characteristics of free-burning wildland fires have significant economic, safety, and environmental impacts. Additionally, the increase in global warming has led to an increase in the number and severity of wildfires. Hence, there is an increasing need for accurately calculating the probability of wildfire propagation in certain areas. In this study, we firstly demonstrate that the landscapes of wildfire propagation can be represented as a scale-free network, where the wildfire is modeled as the scale-free network whose degree follows the power law. By establishing the state-related concepts and modifying the Binary-Addition-Tree (BAT) together with the PageRank, we propose a new methodology to serve as a reliable tool in predicting the probability of wildfire propagation in certain areas. Furthermore, we demonstrate that the proposed maximum-state PageRank used in the methodology can be implemented separately as a fast, simple, and effective tool in identifying the areas that require immediate protection. The proposed methodology and maximum-state PageRank are validated in the example generated from the Barabási-Albert model in the study.

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