scholarly journals A Novel BA Complex Network Model on Color Template Matching

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Risheng Han ◽  
Shigen Shen ◽  
Guangxue Yue ◽  
Hui Ding
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Template Matching ◽  
Preferential Attachment ◽  
Color Space ◽  
Scale Free Network ◽  
Color Distribution ◽  
Scale Free ◽  
Matching Process ◽  
Free Network

A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template’s color distribution. And then the template’s BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching.

scholarly journals HIERARCHICAL AND MIXING PROPERTIES OF STATIC COMPLEX NETWORKS EMERGING FROM FLUCTUATING CLASSICAL RANDOM GRAPHS

2006 ◽  
Vol 17 (09) ◽  
pp. 1303-1311 ◽  
Author(s):  
SUMIYOSHI ABE ◽  
STEFAN THURNER
Keyword(s):  
Complex Network ◽  
Preferential Attachment ◽  
Hierarchical Organization ◽  
Scale Free Network ◽  
Mixing Properties ◽  
Scale Free ◽  
Free Network ◽  
Growing Network ◽  
Arbitrary Connectivity

The Erdös–Rényi classical random graph is characterized by a fixed linking probability for all pairs of vertices. Here, this concept is generalized by drawing the linking probability from a certain distribution. Such a procedure is found to lead to a static complex network with an arbitrary connectivity distribution. In particular, a scale-free network with the hierarchical organization is constructed without assuming any knowledge about the global linking structure, in contrast to the preferential attachment rule for a growing network. The hierarchical and mixing properties of the static scale-free network thus constructed are studied. The present approach establishes a bridge between a scalar characterization of individual vertices and topology of an emerging complex network. The result may offer a clue for understanding the origin of a few abundance of connectivity distributions in a wide variety of static real-world networks.

Scale‐free network model for template matching based on kernel histogram

2014 ◽  
Vol 50 (9) ◽  
pp. 669-671 ◽  
Author(s):  
Ri‐Sheng Han ◽  
Shi‐Gen Shen ◽  
Guang‐Xue Yue ◽  
Hui Ding
Keyword(s):  
Network Model ◽  
Template Matching ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

scholarly journals The Fractional Preferential Attachment Scale-Free Network Model

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 509
Author(s):  
Rafał Rak ◽  
Ewa Rak
Keyword(s):  
Network Model ◽  
Real World ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Clustering Coefficient ◽  
Node Degree ◽  
Scale Free Network ◽  
Scale Free ◽  
Degree Correlation ◽  
Free Network

Many networks generated by nature have two generic properties: they are formed in the process of preferential attachment and they are scale-free. Considering these features, by interfering with mechanism of the preferential attachment, we propose a generalisation of the Barabási–Albert model—the ’Fractional Preferential Attachment’ (FPA) scale-free network model—that generates networks with time-independent degree distributions p ( k ) ∼ k − γ with degree exponent 2 < γ ≤ 3 (where γ = 3 corresponds to the typical value of the BA model). In the FPA model, the element controlling the network properties is the f parameter, where f ∈ ( 0 , 1 ⟩ . Depending on the different values of f parameter, we study the statistical properties of the numerically generated networks. We investigate the topological properties of FPA networks such as degree distribution, degree correlation (network assortativity), clustering coefficient, average node degree, network diameter, average shortest path length and features of fractality. We compare the obtained values with the results for various synthetic and real-world networks. It is found that, depending on f, the FPA model generates networks with parameters similar to the real-world networks. Furthermore, it is shown that f parameter has a significant impact on, among others, degree distribution and degree correlation of generated networks. Therefore, the FPA scale-free network model can be an interesting alternative to existing network models. In addition, it turns out that, regardless of the value of f, FPA networks are not fractal.

The Barabási and Albert scale-free network model

2018 ◽  
Vol 35 (1) ◽  
pp. 123-132 ◽  
Author(s):  
Lei Zhu ◽  
Lei Wang ◽  
Xiang Zheng ◽  
Yuzhang Xu
Keyword(s):  
Network Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

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.

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 Geography in a scale-free network model

2002 ◽  
Vol 66 (5) ◽  
Author(s):  
C. P. Warren ◽  
L. M. Sander ◽  
I. M. Sokolov
Keyword(s):  
Network Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

scholarly journals A Multilevel Simplification Algorithm for Computing the Average Shortest-Path Length of Scale-Free Complex Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Guoyong Mao ◽  
Ning Zhang
Keyword(s):  
Complex Network ◽  
Shortest Path ◽  
Path Length ◽  
Large Scale ◽  
Computation Time ◽  
Scale Free Network ◽  
Original Network ◽  
Memory Space ◽  
Scale Free ◽  
Free Network

Computing the average shortest-path length (ASPL) of a large scale-free network needs much memory space and computation time. Based on the feature of scale-free network, we present a simplification algorithm by cutting the suspension points and the connected edges; the ASPL of the original network can be computed through that of the simplified network. We also present a multilevel simplification algorithm to get ASPL of the original network directly from that of the multisimplified network. Our experiment shows that these algorithms require less memory space and time in computing the ASPL of scale-free network, which makes it possible to analyze large networks that were previously impossible due to memory limitations.

Sign in / Sign up

Export Citation Format

Share Document