A novel method of generating tunable underlying network topologies for social simulation

Вaргa, Имре; Németh, András; Kocsis, Gergely

doi:10.1109/coginfocom.2013.6719189

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…With the intent of studying the topological properties of real networks, several network models have been proposed [8,9]. Some researchers propose some novel methods of generating tunable network topologies for social simulation in [11]. The main purpose of this paper is to build a generation process of social network, in which dierent networks can be generated with dierent input parameters.…”

Section: Introductionmentioning

confidence: 99%

A novel method of generating tunable network topologies for social simulation

Zhu¹

2018

J. Stat. Mech.

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The research of social networks has been paid more and more attention by scholars. In this paper, through the features of the establishment of the relationship between people in real social networks, we have proposed two new structural features, which can be seen an extension of the concept of clustering coecient. In view of these structural features, we have also proposed a new network evolution model which is similar to the formation process of social networks. Our model can generate a variety of networks which are similar to real social networks by adjusting some parameters. The new network model and topology features will help us to better study the related issues of social networks.

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Section: Introductionmentioning

confidence: 99%

A novel method of generating tunable network topologies for social simulation

Zhu¹

2018

J. Stat. Mech.

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“…After the growing period the system undergoes a destroying procedure where independently chosen nodes and their connections are removed. I used the so called general attack process [5] which means that all the nodes has the same probability to be removed. The strength Á of this reduction process can be characterized by the ratio of number of removed nodes N and the original number of nodes at the end of growing phase, so Á D N=N.…”

Section: Extended Modelmentioning

confidence: 99%

“…Models based on "small-world" networks of Watts and Strogatz [2] do not reproduce the power law degree distribution. Most of growing scale-free network models result low clustering coefficients [3][4][5]. There are some trials to create scale-free networks with tunable clustering [6][7][8][9], but in these models the desired value of clustering coefficient determines other properties of the networks.…”

Section: Introductionmentioning

confidence: 99%

Scale-Free Network Topologies with Clustering Similar to Online Social Networks

Вaргa

2015

Springer Proceedings in Complexity

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In this paper I propose a novel method to model real online social networks where the growing scale-free networks have tunable clustering coefficient independently of the average degree and the exponent of the degree distribution. Models based on purely preferential attachment are not able to describe high clustering coefficient of social networks. Beside the attractive popularity my model is based on the fact that if a person knows somebody, probably knows several individuals from his/her acquaintanceship as well. The topological properties of these complex systems were studied and it was found that in my networks the cliques are relevant independently of the system size as usual in social systems.

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“…The so called small-world networks developed by Watts and Strogratz [2] cannot give back power law degree distribution. Growing scale-free network models lead to low clustering [3,4,5]. Some scale-free models with tunable clustering coefficient [6,7,8] capacity.…”

Section: Introductionmentioning

confidence: 99%

Novel model of social networks with tunable clustering coefficient

Вaргa

Kocsis

2015

Proceedings of the 9th International Conference on Applied Informatics, Volume 2

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We propose a novel method to generate scale-free networks with discretely tunable clustering coefficient in order to model real social networks. Recently several methods were introduced to generate networks with power law degree distribution. Most of them are based on preferential attachment, but in these networks the average clustering coefficient is low opposite to the real social networks. Beside the attractive popularity our model is based on the fact that if a person knows somebody, probably knows several people from his/her acquaintanceship as well. The topological properties of these networks were studied and it was found that in these networks the cliques are relevant independently from the system size.

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A novel method of generating tunable underlying network topologies for social simulation

Cited by 4 publications

References 14 publications

A novel method of generating tunable network topologies for social simulation

A novel method of generating tunable network topologies for social simulation

Scale-Free Network Topologies with Clustering Similar to Online Social Networks

Novel model of social networks with tunable clustering coefficient

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