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.

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.

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 A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Tomas Hruz ◽  
Markus Wyss ◽  
Christoph Lucas ◽  
Oliver Laule ◽  
Peter von Rohr ◽  
...  
Keyword(s):  
Complex System ◽  
Systems Biology ◽  
Complex Networks ◽  
Network Structure ◽  
Biological Networks ◽  
Large Graphs ◽  
Layout Algorithm ◽  
Underlying Network ◽  
Visualization Algorithms ◽  
Edge Crossings

Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs.

Control of communication networks: welfare maximization and multipath transfers

2008 ◽  
Vol 366 (1872) ◽  
pp. 1-18 ◽  
Author(s):  
Peter B Key ◽  
Laurent Massoulié
Keyword(s):  
Communication Networks ◽  
Control Strategies ◽  
Transport Layer ◽  
Transport Protocols ◽  
Open Problems ◽  
Network Resource ◽  
Layered Architecture ◽  
Welfare Maximization ◽  
Underlying Network ◽  
In Series

We discuss control strategies for communication networks such as the Internet. We advocate the goal of welfare maximization as a paradigm for network resource allocation. We explore the application of this paradigm to the case of parallel network paths. We show that welfare maximization requires active balancing across paths by data sources, and potentially requires implementation of novel transport protocols. However, the only requirement from the underlying ‘network layer’ is to expose the marginal congestion cost of network paths to the ‘transport layer’. We further illustrate the versatility of the corresponding layered architecture by describing transport protocols with the following properties: they achieve welfare maximization when each communication may use an arbitrary collection of paths; available in an overlay; and are combined in series and parallel. We conclude by commenting on incentives, pricing and open problems.

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.

Modeling and Simulation on Knowledge Transfer among Unofficial Groups Based on Communication Networks

2013 ◽  
Vol 411-414 ◽  
pp. 145-151
Author(s):  
Xiao Dong Kou ◽  
Bo Zhang ◽  
Lin Yang
Keyword(s):  
Knowledge Transfer ◽  
Complex Networks ◽  
Communication Networks ◽  
Simulation Analysis ◽  
China National Knowledge Infrastructure ◽  
Simulation Research ◽  
Knowledge Infrastructure ◽  
Learned Societies ◽  
Agent Simulation ◽  
Multi Agent

With features of good interactivity and fast spread speed, unofficial networks play a significant role in knowledge transfer. Based on theories of communication networks and computational modeling method, the transfer situation of complex networks theory within Chinas learned societies, including its rising, spread and development, was modeled and then made simulation analysis by using the Blanche software. By comparing the analysis results with periodicals data from China National Knowledge Infrastructure, the effectiveness of the built model and the reliability of Blanche in multi-agent simulation research are all validated. Furthermore, the future development of complex networks theory in China is predicted as well.

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.

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.

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