scholarly journals Complex Networks: Traffic Dynamics, Network Performance, and Network Structure

2013 ◽  
Vol 03 (01) ◽  
pp. 187-195 ◽  
Author(s):  
Ziping Hu ◽  
Krishnaiyan Thulasiraman ◽  
Pramode K. Verma
Keyword(s):  
Complex Networks ◽  
Network Structure ◽  
Network Performance ◽  
Traffic Dynamics

scholarly journals Traffic Dynamics on Complex Networks: A Survey

2012 ◽  
Vol 2012 ◽  
pp. 1-23 ◽  
Author(s):  
Shengyong Chen ◽  
Wei Huang ◽  
Carlo Cattani ◽  
Giuseppe Altieri
Keyword(s):  
Complex Networks ◽  
Communication Networks ◽  
Network Structure ◽  
Traffic Dynamics ◽  
Open Problems ◽  
Transport Efficiency ◽  
Traffic Routing ◽  
Underlying Network ◽  
Practical Implications

Traffic dynamics on complex networks are intriguing in recent years due to their practical implications in real communication networks. In this survey, we give a brief review of studies on traffic routing dynamics on complex networks. Strategies for improving transport efficiency, including designing efficient routing strategies and making appropriate adjustments to the underlying network structure, are introduced in this survey. Finally, a few open problems are discussed in this survey.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

Improved influential nodes identification in complex networks

2021 ◽  
pp. 1-9
Author(s):  
Shi Dong ◽  
Wengang Zhou
Keyword(s):  
Theoretical Analysis ◽  
Complex Networks ◽  
Network Structure ◽  
Information Entropy ◽  
Network System ◽  
Weighted Degree ◽  
Identification Methods ◽  
Hybrid Mechanism ◽  
Influential Nodes ◽  
Influential Node

Influential node identification plays an important role in optimizing network structure. Many measures and identification methods are proposed for this purpose. However, the current network system is more complex, the existing methods are difficult to deal with these networks. In this paper, several basic measures are introduced and discussed and we propose an improved influential nodes identification method that adopts the hybrid mechanism of information entropy and weighted degree of edge to improve the accuracy of identification (Hm-shell). Our proposed method is evaluated by comparing with nine algorithms in nine datasets. Theoretical analysis and experimental results on real datasets show that our method outperforms other methods on performance.

Improved efficient routing strategy on two-layer complex networks

2016 ◽  
Vol 27 (04) ◽  
pp. 1650044 ◽  
Author(s):  
Jinlong Ma ◽  
Weizhan Han ◽  
Qing Guo ◽  
Shuai Zhang ◽  
Junfang Wang ◽  
...  
Keyword(s):  
Complex Networks ◽  
Research Topic ◽  
Global Awareness ◽  
Traffic Dynamics ◽  
Shortest Path Routing ◽  
Transport Efficiency ◽  
Routing Strategy ◽  
Traffic Capacity ◽  
Traffic Performance ◽  
Simulation Results

The traffic dynamics of multi-layer networks has become a hot research topic since many networks are comprised of two or more layers of subnetworks. Due to its low traffic capacity, the traditional shortest path routing (SPR) protocol is susceptible to congestion on two-layer complex networks. In this paper, we propose an efficient routing strategy named improved global awareness routing (IGAR) strategy which is based on the betweenness centrality of nodes in the two layers. With the proposed strategy, the routing paths can bypass hub nodes of both layers to enhance the transport efficiency. Simulation results show that the IGAR strategy can bring much better traffic capacity than the SPR and the global awareness routing (GAR) strategies. Because of the significantly improved traffic performance, this study is helpful to alleviate congestion of the two-layer complex networks.

An Introduction to Complex Networks

2013 ◽  
Author(s):  
Jordi Bascompte ◽  
Pedro Jordano
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Network Structure ◽  
Ecological Networks ◽  
Broad Context ◽  
Network Approach ◽  
Mutualistic Networks ◽  
Plant Animal Interactions

Mutualisms can involve dozens, even hundreds, of species and this complexity has precluded a serious community-wide approach to plant–animal interactions. The most straightforward way to describe such an interacting community is with a network of interactions. In this approach, species are represented as nodes of two types: plants and animals. This chapter provides the tools and concepts for characterizing mutualistic networks and placing them into a broad context. This serves as a background with which to understand the structure of mutualistic networks. The discussions cover a network approach to complex systems, measures of network structure, models of network buildup, and ecological networks.

Optimal resource allocation strategy for two-layer complex networks

2018 ◽  
Vol 32 (05) ◽  
pp. 1850054 ◽  
Author(s):  
Jinlong Ma ◽  
Lixin Wang ◽  
Sufeng Li ◽  
Congwen Duan ◽  
Yu Liu
Keyword(s):  
Complex Networks ◽  
Service Providers ◽  
Network Capacity ◽  
Capacity Allocation ◽  
Allocation Strategy ◽  
Traffic Dynamics ◽  
Traffic Capacity ◽  
Optimal Value ◽  
Optimal Resource ◽  
Resource Allocation Strategy

We study the traffic dynamics on two-layer complex networks, and focus on its delivery capacity allocation strategy to enhance traffic capacity measured by the critical value [Formula: see text]. With the limited packet-delivering capacity, we propose a delivery capacity allocation strategy which can balance the capacities of non-hub nodes and hub nodes to optimize the data flow. With the optimal value of parameter [Formula: see text], the maximal network capacity is reached because most of the nodes have shared the appropriate delivery capacity by the proposed delivery capacity allocation strategy. Our work will be beneficial to network service providers to design optimal networked traffic dynamics.

scholarly journals A novel optical network model for network burden release and congestion reduction based on passive optical network

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880765
Author(s):  
He Li ◽  
Hongxi Yin ◽  
Shanshan Lin
Keyword(s):  
Data Storage ◽  
Network Structure ◽  
Network Performance ◽  
Optical Network ◽  
Clustering Coefficient ◽  
Passive Optical Network ◽  
Metropolitan Area Networks ◽  
Performance Simulation ◽  
Storage Devices ◽  
Optical Line

In this article, a new optical network structure coping with network congestion is proposed, which is based on passive optical network, and adopts data storage devices coupled with optical line terminal to release network burden. It is shown by our network performance simulation that this network has many merits such as free-scale, fewer connections, larger clustering coefficient, and smaller average shortest path length in comparison to the passive optical network. The novel network structure can replace the far-end service connections with the near-end ones, reduce congestions in an optical network, and, furthermore, relieve traffic burden in optical backbone and metropolitan area networks.

scholarly journals Control of epidemic spreading on complex networks by local traffic dynamics

2011 ◽  
Vol 84 (4) ◽  
Author(s):  
Han-Xin Yang ◽  
Wen-Xu Wang ◽  
Ying-Cheng Lai ◽  
Yan-Bo Xie ◽  
Bing-Hong Wang
Keyword(s):  
Complex Networks ◽  
Epidemic Spreading ◽  
Traffic Dynamics

Optimizing complex networks controllability by local structure information

2016 ◽  
Vol 27 (10) ◽  
pp. 1650115
Author(s):  
Houyi Yan ◽  
Lvlin Hou ◽  
Yunxiang Ling ◽  
Guohua Wu
Keyword(s):  
Numerical Simulation ◽  
Complex Networks ◽  
Network Structure ◽  
Local Structure ◽  
Global Structure ◽  
Directed Network ◽  
Structure Information ◽  
Different Types ◽  
Extensive Numerical Simulation ◽  
Network Controllability

Research in network controllability has mostly been focused on the effects of the network structure on its controllability, and some methods have been proposed to optimize the network controllability. However, they are all based on global structure information of networks. We propose two different types of methods to optimize controllability of a directed network by local structure information. Extensive numerical simulation on many modeled networks demonstrates that this method is effective. Since the whole topologies of many real networks are not visible and we only get some local structure information, this strategy is potentially more practical.

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