scholarly journals A random interacting network model for complex networks

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Bedartha Goswami ◽  
Snehal M. Shekatkar ◽  
Aljoscha Rheinwalt ◽  
G. Ambika ◽  
Jürgen Kurths
Keyword(s):  
Complex Networks ◽  
Network Model

scholarly journals Synchronization in a Novel Local-World Dynamical Network Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jianeng Tang ◽  
Peizhong Liu
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Network Model ◽  
Optimal Growth ◽  
Dynamical Networks ◽  
Dynamical Network ◽  
Network Research ◽  
Topology Model ◽  
The Relationship ◽  
Random Failures

Advances in complex network research have recently stimulated increasing interests in understanding the relationship between the topology and dynamics of complex networks. In the paper, we study the synchronizability of a class of local-world dynamical networks. Then, we have proposed a local-world synchronization-optimal growth topology model. Compared with the local-world evolving network model, it exhibits a stronger synchronizability. We also investigate the robustness of the synchronizability with respect to random failures and the fragility of the synchronizability with specific removal of nodes.

Network model of bilateral power markets based on complex networks

2014 ◽  
Vol 28 (22) ◽  
pp. 1450144 ◽  
Author(s):  
Yang Wu ◽  
Junyong Liu ◽  
Furong Li ◽  
Zhanxin Yan ◽  
Li Zhang
Keyword(s):  
Complex Networks ◽  
Network Model ◽  
Simulation Analysis ◽  
Electric Power Industry ◽  
Clustering Coefficient ◽  
Power Markets ◽  
Cascading Failure ◽  
Topological Stability ◽  
Market Organization ◽  
Topological Robustness

The bilateral power transaction (BPT) mode becomes a typical market organization with the restructuring of electric power industry, the proper model which could capture its characteristics is in urgent need. However, the model is lacking because of this market organization's complexity. As a promising approach to modeling complex systems, complex networks could provide a sound theoretical framework for developing proper simulation model. In this paper, a complex network model of the BPT market is proposed. In this model, price advantage mechanism is a precondition. Unlike other general commodity transactions, both of the financial layer and the physical layer are considered in the model. Through simulation analysis, the feasibility and validity of the model are verified. At same time, some typical statistical features of BPT network are identified. Namely, the degree distribution follows the power law, the clustering coefficient is low and the average path length is a bit long. Moreover, the topological stability of the BPT network is tested. The results show that the network displays a topological robustness to random market member's failures while it is fragile against deliberate attacks, and the network could resist cascading failure to some extent. These features are helpful for making decisions and risk management in BPT markets.

Building Complex Network Similar to Facebook

2014 ◽  
Vol 513-517 ◽  
pp. 909-913
Author(s):  
Dong Wei Guo ◽  
Xiang Yan Meng ◽  
Cai Fang Hou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Complex Networks ◽  
Complex Network ◽  
Network Model ◽  
Clustering Coefficient ◽  
Network Characteristics ◽  
Modeling Methods ◽  
Community Strength ◽  
Strength Based

Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

scholarly journals Topological Properties of a 3-Regular Small World Network

2014 ◽  
Vol 2014 ◽  
pp. 1-4
Author(s):  
Huanshen Jia ◽  
Guona Hu ◽  
Haixing Zhao
Keyword(s):  
Complex Networks ◽  
Network Model ◽  
Spanning Trees ◽  
Small World ◽  
Clustering Coefficient ◽  
Deterministic Models ◽  
Engineering Technology ◽  
Research Fields ◽  
Potential Applications ◽  
Regular Network

Complex networks have seen much interest from all research fields and have found many potential applications in a variety of areas including natural, social, biological, and engineering technology. The deterministic models for complex networks play an indispensable role in the field of network model. The construction of a network model in a deterministic way not only has important theoretical significance, but also has potential application value. In this paper, we present a class of 3-regular network model with small world phenomenon. We determine its relevant topological characteristics, such as diameter and clustering coefficient. We also give a calculation method of number of spanning trees in the 3-regular network and derive the number and entropy of spanning trees, respectively.

scholarly journals Noncommutative network models

2019 ◽  
Vol 30 (1) ◽  
pp. 14-32
Author(s):  
Joe Moeller
Keyword(s):  
Complex Networks ◽  
Network Model ◽  
Network Models ◽  
Graph Products ◽  
Bounded Degree ◽  
Simple Graphs ◽  
Products Of Groups ◽  
Free Network ◽  
Model Networks

AbstractNetwork models, which abstractly are given by lax symmetric monoidal functors, are used to construct operads for modeling and designing complex networks. Many common types of networks can be modeled with simple graphs with edges weighted by a monoid. A feature of the ordinary construction of network models is that it imposes commutativity relations between all edge components. Because of this, it cannot be used to model networks with bounded degree. In this paper, we construct the free network model on a given monoid, which can model networks with bounded degree. To do this, we generalize Green’s graph products of groups to pointed categories which are finitely complete and cocomplete.

Cluster Mixed Synchronization of Complex Networks with Disturbance

2015 ◽  
Vol 719-720 ◽  
pp. 448-451
Author(s):  
Li Jie Zeng
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Numerical Simulations ◽  
Network Model ◽  
Complex Dynamical Networks ◽  
Time Varying ◽  
Dynamical Networks ◽  
Bounded Disturbances ◽  
Synchronization Scheme ◽  
Theoretical Results

In this paper, we investigate the cluster mixed synchronization scheme in time-varying delays coupled complex dynamical networks with disturbance. Basing on the community structure of the networks, some sufficient criteria are derived to ensure cluster mixed synchronization of the network model. Particularly, unknown bounded disturbances can be conquered by the proposed control. The numerical simulations are performed to verify the effectiveness of the theoretical results

Modelling and Analysis of River Networks Based on Complex Networks Theory

2013 ◽  
Vol 756-759 ◽  
pp. 2728-2733 ◽  
Author(s):  
Xue Wen Wu ◽  
Ling Li ◽  
Yong Gang Qu
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
River Basin ◽  
Network Model ◽  
Small World ◽  
River Networks ◽  
Haihe River Basin ◽  
Haihe River ◽  
New Approach ◽  
And Control

River systems are open and self-organizing complex systems. Complex networks theory can well combine rivers' macro properties with their microscopic properties. This paper builds a river network model based on complex networks theory and describes its characteristics. After the analysis of the model used in Haihe River Basin, it shows that Haihe River Basin network has the small-world characteristics. This work provides a new approach to research the properties of river networks, so that to predict and control its behavior.

FROM A HARMONIOUS UNIFYING HYBRID PREFERENTIAL MODEL TOWARD A LARGE UNIFYING HYBRID NETWORK MODEL

2007 ◽  
Vol 21 (30) ◽  
pp. 5121-5142 ◽  
Author(s):  
JINQING FANG ◽  
YONG LI ◽  
QIAO BI
Keyword(s):  
Complex Networks ◽  
Network Model ◽  
Random Network ◽  
Network Models ◽  
Hybrid Network ◽  
Topological Properties ◽  
Degree Correlation ◽  
Meaningful Result ◽  
Satisfactory Answer ◽  
New Phenomena

The motivation of this work raises four challenging questions: (1) Why is it that so many generalized random network models exist but they cannot be completely consistent with real-world networks? (2) Are these complex networks fundamentally attached in a random preferential manner without any deterministic attachment for both un-weighted and weighted networks? To answer the first two questions, we propose a harmonious unifying hybrid preferential model (HUHPM) controlled by a total hybrid ratio. (3) Why are social networks mostly positive degree-degree correlation but biological and technological networks tend to possess negative degree-degree correlation? (4) Are there coherent physical ideas and a unification formation mechanism for studies of complex networks? To seek a better answer of all these questions, especially the last two above, we extend the HUHPM to a large unifying hybrid network model (LUHNM), based on introducing two new hybrid ratios. We study the two models above, both numerically and analytically. All findings of topological properties in the network models above can give a certain universally meaningful result, which reveals some nontrivial topological properties, new phenomena, and give a relatively satisfactory answer.

scholarly journals STRENGTH DISTRIBUTION IN DERIVATIVE NETWORKS

2005 ◽  
Vol 16 (07) ◽  
pp. 1097-1105 ◽  
Author(s):  
LUCIANO DA FONTOURA COSTA ◽  
GONZALO TRAVIESO
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Lower Limit ◽  
Network Model ◽  
Power Law ◽  
Neuronal Networks ◽  
Strength Distribution ◽  
Node Degree ◽  
Upper Limit ◽  
The Difference

This article describes a complex network model whose weights are proportional to the difference between uniformly distributed "fitness" values assigned to the nodes. It is shown both analytically and experimentally that the strength density (i.e., the weighted node degree) for this model, called derivative complex networks, follows a power law with exponent γ<1 if the fitness has an upper limit and γ>1 if the fitness has no upper limit but a positive lower limit. Possible implications for neuronal networks topology and dynamics are also discussed.

Limit of a nonpreferential attachment multitype network model

2017 ◽  
Vol 31 (05) ◽  
pp. 1750026 ◽  
Author(s):  
Yilun Shang
Keyword(s):  
Complex Networks ◽  
Network Model ◽  
Theoretical Framework ◽  
Network Size ◽  
Temporal Networks ◽  
Limit Theory ◽  
Time Order ◽  
Graph Limit

Here, we deal with a model of multitype network with nonpreferential attachment growth. The connection between two nodes depends asymmetrically on their types, reflecting the implication of time order in temporal networks. Based upon graph limit theory, we analytically determined the limit of the network model characterized by a kernel, in the sense that the number of copies of any fixed subgraph converges when network size tends to infinity. The results are confirmed by extensive simulations. Our work thus provides a theoretical framework for quantitatively understanding grown temporal complex networks as a whole.

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