Integrating network structure and dynamic information for better routing strategy on scale-free networks

2009 ◽  
Vol 388 (12) ◽  
pp. 2547-2554 ◽  
Author(s):  
Xiao-Gai Tang ◽  
Eric W.M. Wong ◽  
Zhi-Xi Wu
Keyword(s):  
Network Structure ◽  
Dynamic Information ◽  
Scale Free ◽  
Routing Strategy ◽  
Scale Free Networks

Heterogeneous delivering capability promotes traffic efficiency in complex networks

2015 ◽  
Vol 26 (06) ◽  
pp. 1550069
Author(s):  
Yan-Bo Zhu ◽  
Xiang-Min Guan ◽  
Xue-Jun Zhang
Keyword(s):  
Network Capacity ◽  
Dynamic Routing ◽  
Transportation Systems ◽  
Networked Systems ◽  
Dynamic Information ◽  
Scale Free ◽  
Traffic Efficiency ◽  
Global Dynamic ◽  
Routing Strategy ◽  
Scale Free Networks

Traffic is one of the most fundamental dynamical processes in networked systems. With the homogeneous delivery capability of nodes, the global dynamic routing strategy proposed by Ling et al. [Phys. Rev. E81, 016113 (2010)] adequately uses the dynamic information during the process and thus it can reach a quite high network capacity. In this paper, based on the global dynamic routing strategy, we proposed a heterogeneous delivery allocation strategy of nodes on scale-free networks with consideration of nodes degree. It is found that the network capacity as well as some other indexes reflecting transportation efficiency are further improved. Our work may be useful for the design of more efficient routing strategies in communication or transportation systems.

scholarly journals Effective gravitation path routing strategy on scale-free networks

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jinlong Ma ◽  
Junfeng Zhang ◽  
Yongqiang Zhang
Keyword(s):  
Scale Free ◽  
Routing Strategy ◽  
Scale Free Networks

scholarly journals Distance-Based Routing Strategy for Traffic Transport in Spatial Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Wei Huang ◽  
Xiang Pan ◽  
Xi Yang ◽  
Jianhua Zhang
Keyword(s):  
Spatial Scale ◽  
Large Scale ◽  
Spatial Information ◽  
Good Choice ◽  
Spatial Distance ◽  
Greedy Routing ◽  
Scale Free ◽  
Routing Strategy ◽  
Scale Free Networks ◽  
Large Scale Networks

It is well known that routing strategies based on global topological information is not a good choice for the enhancement of traffic throughput in large-scale networks due to the heavy communication cost. On the contrary, acquiring spatial information, such as spatial distances among nodes, is more feasible. In this paper, we propose a novel distance-based routing strategy in spatial scale-free networks, called LDistance strategy. The probability of establishing links among nodes obeys the power-law in the spatial network under study. Compared with the LDegree strategy (Wang et al., 2006) and the mixed strategy (a strategy combining both greedy routing strategy and random routing strategy), results show that our proposed LDistance strategy can further enhance traffic capacity. Besides, the LDistance strategy can also achieve a much shorter delivering time than the LDegree strategy. Analyses reveal that the superiority of our strategy is mainly due to the interdependent relationship between topological and spatial characteristics in spatial scale-free networks. Furthermore, along transporting path in the LDistance strategy, the spatial distance to destination decays more rapidly, and the degrees of routers are higher than those in the LDegree strategy.

Generalized minimum information path routing strategy on scale-free networks

2011 ◽  
Vol 20 (8) ◽  
pp. 080501 ◽  
Author(s):  
Si-Yuan Zhou ◽  
Kai Wang ◽  
Yi-Feng Zhang ◽  
Wen-Jiang Pei ◽  
Cun-Lai Pu ◽  
...  
Keyword(s):  
Minimum Information ◽  
Scale Free ◽  
Routing Strategy ◽  
Scale Free Networks

EFFECTS OF CONFIDENCE SCALE AND NETWORK STRUCTURE ON MINORITY OPINION SPREADING

2010 ◽  
Vol 21 (08) ◽  
pp. 1001-1010 ◽  
Author(s):  
BO SHEN ◽  
YUN LIU
Keyword(s):  
Simple Model ◽  
Network Structure ◽  
Random Network ◽  
Random Networks ◽  
Scale Free Network ◽  
Opinion Formation ◽  
Scale Free ◽  
Scale Free Networks ◽  
Free Network ◽  
Confidence Scale

We study the dynamics of minority opinion spreading using a proposed simple model, in which the exchange of views between agents is determined by a quantity named confidence scale. To understand what will promote the success of minority, two types of networks, random network and scale-free network are considered in opinion formation. We demonstrate that the heterogeneity of networks is advantageous to the minority and exchanging views between more agents will reduce the opportunity of minority's success. Further, enlarging the degree that agents trust each other, i.e. confidence scale, can increase the probability that opinions of the minority could be accepted by the majority. We also show that the minority in scale-free networks are more sensitive to the change of confidence scale than that in random networks.

Check-in based routing strategy in scale-free networks

2017 ◽  
Vol 468 ◽  
pp. 205-211 ◽  
Author(s):  
Zhong-Yuan Jiang ◽  
Jian-Feng Ma ◽  
Yu-Long Shen
Keyword(s):  
Scale Free ◽  
Routing Strategy ◽  
Scale Free Networks

Probability routing strategy for scale-free networks

2013 ◽  
Vol 392 (4) ◽  
pp. 953-958 ◽  
Author(s):  
Xiaojun Zhang ◽  
Zishu He ◽  
Zheng He ◽  
Lez Rayman-Bacchus
Keyword(s):  
Scale Free ◽  
Routing Strategy ◽  
Scale Free Networks

THE GINI COEFFICIENT'S APPLICATION TO GENERAL COMPLEX NETWORKS

2005 ◽  
Vol 08 (01) ◽  
pp. 159-167 ◽  
Author(s):  
HAI-BO HU ◽  
LIN WANG
Keyword(s):  
Income Inequality ◽  
Complex Networks ◽  
Network Structure ◽  
Gini Coefficient ◽  
Scale Free ◽  
The Gini Coefficient ◽  
Scale Free Networks ◽  
General Complex

The Gini coefficient, which was originally used in microeconomics to describe income inequality, is introduced into the research of general complex networks as a metric on the heterogeneity of network structure. Some parameters such as degree exponent and degree-rank exponent were already defined in the case of scale-free networks also as a metric on the heterogeneity. In scale-free networks, the Gini coefficient is proved to be equivalent to the parameters mentioned above, and moreover, a classification of infinite scale-free networks is given according to the value of the Gini coefficient.

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