A meta-analysis of centrality measures for comparing and generating complex network models

2016 ◽  
Vol 17 ◽  
pp. 205-215 ◽  
Author(s):  
Kyle Robert Harrison ◽  
Mario Ventresca ◽  
Beatrice M. Ombuki-Berman
Keyword(s):  
Complex Network ◽  
Meta Analysis ◽  
Network Models ◽  
Centrality Measures ◽  
Complex Network Models

Modern Diffusion of Products with Complex Network Models

2011 ◽  
pp. 119-133
Author(s):  
Atsushi Tanaka
Keyword(s):  
Complex Networks ◽  
Marketing Strategy ◽  
Complex Network ◽  
Network Models ◽  
Information Propagation ◽  
Multiagent Simulation ◽  
Product Marketing ◽  
Winner Takes All ◽  
Complex Network Models

In this chapter, some important matters of complex networks and their models are reviewed shortly, and then the modern diffusion of products under the information propagation using multiagent simulation is discussed. The remarkable phenomena like “Winner-Takes-All” and “Chasm” can be observed, and one product marketing strategy is also proposed.

Complex Network Model and its Application

2013 ◽  
Vol 791-793 ◽  
pp. 1589-1592
Author(s):  
Shuai Xu ◽  
Bai Da Zhang
Keyword(s):  
Complex Network ◽  
Road Network ◽  
Human Life ◽  
Network Models ◽  
Physical Object ◽  
Statistical Characteristics ◽  
Power Network ◽  
The Stability ◽  
Complex Network Models ◽  
Important Significance

Human life is in a complex network world. In everyday life, the network can be a physical object such as the Internet, power network, road network and neural network; can also abstract not touch, such as interpersonal networks, networks of co-operation in scientific research, product supply chain network, biological populations, networks, etc.. The topology of these networks, the statistical characteristics and the formation mechanism, and so on, has a very important significance for the efficient allocation of resources, provides various functions, as well as the stability of the network, however, due to the complexity of these networks, conventional simplified model and cannot be good solution to the above problems. The complex network and network complexity has become a hot issue in the scientific and engineering concern. This article describes a few common complex network models and its application brief.

scholarly journals Differentiating complex network models: An engineering perspective

2012 ◽  
Vol 64 (5) ◽  
pp. 840-848 ◽  
Author(s):  
Zhengping Fan ◽  
Guanrong Chen ◽  
Yunong Zhang
Keyword(s):  
Complex Network ◽  
Network Models ◽  
Complex Network Models

The Impact of Complex Network Models to EDAs

2014 ◽  
Vol 926-930 ◽  
pp. 3290-3293
Author(s):  
Cai Chang Ding ◽  
Wen Xiu Peng ◽  
Wei Ming Wang
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Optimization Problem ◽  
Wide Spectrum ◽  
Network Models ◽  
Problem Structure ◽  
Topological Characteristics ◽  
Complex Network Models ◽  
The Impact

The study conducted in this paper is mainly driven by the topological characteristics of the structures that the interactions among the variables of the problems provide. Taking as reference the emergent field of complex networks, we generate a wide spectrum of networks that will serve as problem structures. Then, the impact that the topological characteristics of those networks have, both in the hardness of the optimization problem and in the behavior of the EDA, is analyzed. This reveals a relationship among the topology of the problem structure, the difficulty of the problems and the dependences that the algorithm needs to learn in order to solve the problems.

scholarly journals Cloud macroscopic organization: order emerging from randomness

2011 ◽  
Vol 11 (15) ◽  
pp. 7483-7490 ◽  
Author(s):  
T. Yuan
Keyword(s):  
Complex Network ◽  
Climate Models ◽  
Spatial Scales ◽  
Network Models ◽  
Climate Science ◽  
Power Law Distribution ◽  
Deterministic Models ◽  
New Approach ◽  
Complex Network Models ◽  
Organizing Principles

Abstract. Clouds play a central role in many aspects of the climate system and their forms and shapes are remarkably diverse. Appropriate representation of clouds in climate models is a major challenge because cloud processes span at least eight orders of magnitude in spatial scales. Here we show that there exists order in cloud size distribution of low-level clouds, and that it follows a power-law distribution with exponent γ close to 2. γ is insensitive to yearly variations in environmental conditions, but has regional variations and land-ocean contrasts. More importantly, we demonstrate this self-organizing behavior of clouds emerges naturally from a complex network model with simple, physical organizing principles: random clumping and merging. We also demonstrate symmetry between clear and cloudy skies in terms of macroscopic organization because of similar fundamental underlying organizing principles. The order in the apparently complex cloud-clear field thus has its root in random local interactions. Studying cloud organization with complex network models is an attractive new approach that has wide applications in climate science. We also propose a concept of cloud statistic mechanics approach. This approach is fully complementary to deterministic models, and the two approaches provide a powerful framework to meet the challenge of representing clouds in our climate models when working in tandem.

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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