DISTANCE PREFERENCES SMALL-WORLD COMMUNICATION TOPOLOGY FOR AGENT NETWORK

2010 ◽  
Vol 24 (11) ◽  
pp. 1489-1499
Author(s):  
ZHENGPING WU ◽  
RENMING WANG
Keyword(s):  
Communication Networks ◽  
Path Length ◽  
Network Models ◽  
Small World ◽  
Laplacian Matrix ◽  
Average Path Length ◽  
Small World Networks ◽  
Network Parameter ◽  
Communication Topology ◽  
Multi Agent

In multi-agent system (MAS), the communication topology of agent network plays a very important role in its collaboration. Small-world networks are the networks with high local clustering and small average path length, and the communication networks of MAS can be analyzed within the frame of small-world topology. Yet the real multiagent communication networks are abundant and the classical WS small-world model is not suitable for all cases. In this paper, two new small-world network models are presented. One is based on random graph substrate and local nodes preference reconnection and the other is based on regular graph substrate and long-range nodes preference reconnection. The characteristic of the network parameter such as the clustering coefficients, average path length, and eigenvalue λ2 and λn of the Laplacian matrix for these two models and WS model is studied. The consensus problem that based on these three models is also studied. An example is given and the conclusions are made in the end.

scholarly journals On the Influence of Topological Characteristics on Robustness of Complex Networks

2013 ◽  
Vol 3 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Dharshana Kasthurirathna ◽  
Mahendra Piraveenan ◽  
Gnanakumar Thedchanamoorthy
Keyword(s):  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Small World Networks ◽  
Scale Free ◽  
Rich Club ◽  
Targeted Attacks ◽  
Topological Characteristics ◽  
Topological Robustness

Abstract In this paper, we explore the relationship between the topological characteristics of a complex network and its robustness to sustained targeted attacks. Using synthesised scale-free, small-world and random networks, we look at a number of network measures, including assortativity, modularity, average path length, clustering coefficient, rich club profiles and scale-free exponent (where applicable) of a network, and how each of these influence the robustness of a network under targeted attacks. We use an established robustness coefficient to measure topological robustness, and consider sustained targeted attacks by order of node degree. With respect to scale-free networks, we show that assortativity, modularity and average path length have a positive correlation with network robustness, whereas clustering coefficient has a negative correlation. We did not find any correlation between scale-free exponent and robustness, or rich-club profiles and robustness. The robustness of small-world networks on the other hand, show substantial positive correlations with assortativity, modularity, clustering coefficient and average path length. In comparison, the robustness of Erdos-Renyi random networks did not have any significant correlation with any of the network properties considered. A significant observation is that high clustering decreases topological robustness in scale-free networks, yet it increases topological robustness in small-world networks. Our results highlight the importance of topological characteristics in influencing network robustness, and illustrate design strategies network designers can use to increase the robustness of scale-free and small-world networks under sustained targeted attacks.

scholarly journals Synchronizability of Small-World Networks Generated from a Two-Dimensional Kleinberg Model

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Yi Zhao ◽  
Jianwen Feng ◽  
Jingyi Wang
Keyword(s):  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Small World Network ◽  
Two Dimensional ◽  
Small World Networks

This paper investigates the synchronizability of small-world networks generated from a two-dimensional Kleinberg model, which is more general than NW small-world network. The three parameters of the Kleinberg model, namely, the distance of neighbors, the number of edge-adding, and the edge-adding probability, are analyzed for their impacts on its synchronizability and average path length. It can be deduced that the synchronizability becomes stronger as the edge-adding probability increases, and the increasing edge-adding probability could make the average path length of the Kleinberg small-world network go smaller. Moreover, larger distance among neighbors and more edges to be added could play positive roles in enhancing the synchronizability of the Kleinberg model. The lorentz oscillators are employed to verify the conclusions numerically.

SCALE-FREE AND SMALL-WORLD PROPERTIES OF A SPECIAL HIERARCHICAL NETWORK

Fractals ◽  
2019 ◽  
Vol 27 (02) ◽  
pp. 1950010
Author(s):  
DAOHUA WANG ◽  
YUMEI XUE ◽  
QIAN ZHANG ◽  
MIN NIU
Keyword(s):  
Degree Distribution ◽  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Hierarchical Network ◽  
Scale Free

Many real systems behave similarly with scale-free and small-world structures. In this paper, we generate a special hierarchical network and based on the particular construction of the graph, we aim to present a study on some properties, such as the clustering coefficient, average path length and degree distribution of it, which shows the scale-free and small-world effects of this network.

Complex Systems in a Nutshell

2020 ◽  
pp. 19-32
Author(s):  
Megan S. Patterson ◽  
Michael K. Lemke ◽  
Jordan Nelon
Keyword(s):  
Complex Systems ◽  
Path Length ◽  
Path Dependence ◽  
Small World ◽  
Cardiometabolic Disease ◽  
Small World Networks ◽  
Stable States ◽  
Scale Free ◽  
Critical Transitions ◽  
Random Scale

This chapter provides an overview of the key foundational concepts and principles of the study of complex systems. First, a definition for system is provided, and the distinctions between complicated and complex systems are demarcated, as are detail, disorganized, organized, and dynamic types of complexity. Common properties across complex systems are defined and described, including stable states and steady states, path dependence, resilience, critical transitions and tipping points, early warning signals, feedback loops, and nonlinearity. This chapter also delves into how complex issues often consist of networks, with random, scale-free, and small world networks defined and network concepts such as degrees, path length, and heterogeneity defined. The concept of emergence is also emphasized, as well as related principles such as adaptation and self-organization. Cardiometabolic disease (and associated comorbidities) is used in this chapter as a thematic population health example.

EIGENTIME IDENTITY OF THE WEIGHTED KOCH NETWORKS

Fractals ◽  
2018 ◽  
Vol 26 (03) ◽  
pp. 1850042 ◽  
Author(s):  
YU SUN ◽  
JIAHUI ZOU ◽  
MEIFENG DAI ◽  
XIAOQIAN WANG ◽  
HUALONG TANG ◽  
...  
Keyword(s):  
Transition Matrix ◽  
Small World ◽  
Laplacian Matrix ◽  
Block Matrix ◽  
The Other ◽  
Small World Networks ◽  
Eigenvalues And Eigenvectors ◽  
Two Factors ◽  
Analytical Research ◽  
Definition Of

The eigenvalues of the transition matrix of a weighted network provide information on its structural properties and also on some relevant dynamical aspects, in particular those related to biased walks. Although various dynamical processes have been investigated in weighted networks, analytical research about eigentime identity on such networks is much less. In this paper, we study analytically the scaling of eigentime identity for weight-dependent walk on small-world networks. Firstly, we map the classical Koch fractal to a network, called Koch network. According to the proposed mapping, we present an iterative algorithm for generating the weighted Koch network. Then, we study the eigenvalues for the transition matrix of the weighted Koch networks for weight-dependent walk. We derive explicit expressions for all eigenvalues and their multiplicities. Afterwards, we apply the obtained eigenvalues to determine the eigentime identity, i.e. the sum of reciprocals of each nonzero eigenvalues of normalized Laplacian matrix for the weighted Koch networks. The highlights of this paper are computational methods as follows. Firstly, we obtain two factors from factorization of the characteristic equation of symmetric transition matrix by means of the operation of the block matrix. From the first factor, we can see that the symmetric transition matrix has at least [Formula: see text] eigenvalues of [Formula: see text]. Then we use the definition of eigenvalues and eigenvectors to calculate the other eigenvalues.

Complex networks

2018 ◽  
Author(s):  
Graziano Vernizzi ◽  
Henri Orland
Keyword(s):  
Complex Networks ◽  
Small World ◽  
Laplacian Matrix ◽  
Square Lattice ◽  
Small World Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Free Network ◽  
Scale Free Graphs ◽  
Regular Networks

This article deals with complex networks, and in particular small world and scale free networks. Various networks exhibit the small world phenomenon, including social networks and gene expression networks. The local ordering property of small world networks is typically associated with regular networks such as a 2D square lattice. The small world phenomenon can be observed in most scale free networks, but few small world networks are scale free. The article first provides a brief background on small world networks and two models of scale free graphs before describing the replica method and how it can be applied to calculate the spectral densities of the adjacency matrix and Laplacian matrix of a scale free network. It then shows how the effective medium approximation can be used to treat networks with finite mean degree and concludes with a discussion of the local properties of random matrices associated with complex networks.

Resource idiosyncrasy, performance, and inter-firm small-world networks

2014 ◽  
Vol 7 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Jarle Aarstad
Keyword(s):  
Path Length ◽  
Design Methodology ◽  
Empirical Studies ◽  
Small World ◽  
Small World Networks ◽  
Structural Holes ◽  
Content Type ◽  
High Degree ◽  
Practical Implications

Purpose – Many networks take a small-world structure, with a high degree of clustering and shortcut ties that reduce the path-length between the clusters. It can be argued that small-world networks have benefits that are simultaneously related to network closures and the spanning of structural holes, but research on the network members’ performance is nonetheless inconclusive. The purpose of this paper is to argue that the concept of resource idiosyncrasy can explain the mixed findings. Firm idiosyncratic resources are not easily generalizable across enterprises. Design/methodology/approach – Industries may vary in terms of resource idiosyncrasy, and the paper elaborates how this can moderate shortcut ties’ effect on performance in an inter-firm network. Findings – If resource idiosyncrasy predominates in an industry, the paper proposes that inter-firm shortcut ties may increase performance, whereas shortcut ties may decrease performance if non-idiosyncratic resources predominate. Originality/value – Applying the concept of resource idiosyncrasy as a moderating variable, the paper aims to explain shortcut ties’ effect on performance in an inter-firm network. The theory advanced here can have practical implications and also motivate future empirical studies to gain further knowledge about small-world networks’ effect on performance.

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.

THE SMALL-WORLD PROPERTY IN NETWORKS GROWING BY ACTIVE EDGES

2011 ◽  
Vol 14 (06) ◽  
pp. 853-869 ◽  
Author(s):  
PHILIPPE J. GIABBANELLI
Keyword(s):  
Growth Mechanism ◽  
Average Distance ◽  
Network Models ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Networks ◽  
Fractal Approach ◽  
Small World Property ◽  
Complex Network Models ◽  
Similar Growth

In the last three years, we have witnessed an increasing number of complex network models based on a 'fractal' approach, in which parts of the network are repeatedly replaced by a given pattern. Our focus is on models that can be defined by repeatedly adding a pattern network to selected edges, called active edges. We prove that when a pattern network has at least two active edges, then the resulting network has an average distance at most logarithmic in the number of nodes. This suggests that real-world networks based on a similar growth mechanism are likely to have small average distance. We provide an estimate of the clustering coefficient and verify its accuracy using simulations. Using numerous examples of simple patterns, our simulations show various ways to generate small-world networks. Finally, we discuss extensions to our framework encompassing probabilistic patterns and active subnetworks.

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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