scholarly journals The Relations Between Classic and Geometric Probability and Scale-Free Feature In Social Networks

2017 ◽  
Author(s):  
Fei MA ◽  
Jing SU ◽  
Bing YAO
Keyword(s):  
Social Networks ◽  
Geometric Probability ◽  
Scale Free

scholarly journals RECIPROCITY AND THE EMERGENCE OF POWER LAWS IN SOCIAL NETWORKS

2006 ◽  
Vol 17 (07) ◽  
pp. 1067-1076 ◽  
Author(s):  
MICHAEL SCHNEGG
Keyword(s):  
Social Networks ◽  
Power Laws ◽  
Scaling Exponent ◽  
Policy Makers ◽  
Public Health Programs ◽  
Scale Free ◽  
The Past ◽  
Naturally Occurring ◽  
Growing Networks ◽  
Law Degree

Research in network science has shown that many naturally occurring and technologically constructed networks are scale free, that means a power law degree distribution emerges from a growth model in which each new node attaches to the existing network with a probability proportional to its number of links (= degree). Little is known about whether the same principles of local attachment and global properties apply to societies as well. Empirical evidence from six ethnographic case studies shows that complex social networks have significantly lower scaling exponents γ ~ 1 than have been assumed in the past. Apparently humans do not only look for the most prominent players to play with. Moreover cooperation in humans is characterized through reciprocity, the tendency to give to those from whom one has received in the past. Both variables — reciprocity and the scaling exponent — are negatively correlated (r = -0.767, sig = 0.075). If we include this effect in simulations of growing networks, degree distributions emerge that are much closer to those empirically observed. While the proportion of nodes with small degrees decreases drastically as we introduce reciprocity, the scaling exponent is more robust and changes only when a relatively large proportion of attachment decisions follow this rule. If social networks are less scale free than previously assumed this has far reaching implications for policy makers, public health programs and marketing alike.

Coupling entropy of co-processing model on social networks

2015 ◽  
Vol 29 (25) ◽  
pp. 1550149
Author(s):  
Zhanli Zhang
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Structural Characteristics ◽  
Time Lag ◽  
Optimal Routing ◽  
Information Propagation ◽  
Time Processing ◽  
Scale Free ◽  
Routing Strategy ◽  
Processing Ability

Coupling entropy of co-processing model on social networks is investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are achieved to disclose the formation. In order to understand the evolution of the co-processing and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the coupling entropy comprehending the structural characteristics and information propagation on social network. Based on the analysis of the co-processing model, we analyze the coupling impact of the structural factor and information propagating factor on the coupling entropy, where the analytical results fit well with the numerical ones on scale-free social networks.

Optimal Nonlinear Pricing in Social Networks Under Asymmetric Network Information

2020 ◽  
Vol 68 (3) ◽  
pp. 818-833
Author(s):  
Yang Zhang ◽  
Ying-Ju Chen
Keyword(s):  
Social Networks ◽  
Nonlinear Pricing ◽  
Local Network ◽  
Solution Approach ◽  
Scale Free ◽  
Network Information ◽  
Pricing Decisions ◽  
Pricing Scheme ◽  
The Social ◽  
Free Network

How should a firm make pricing decisions in social networks when the customers hold in private their local network information? In “Optimal Nonlinear Pricing in Social Networks Under Asymmetric Network Information,” Zhang and Chen develop a solution approach based on calculus of variations and positive neighbor affiliation to tackle this problem. They show that the optimal pricing compromises the capitalization of the susceptibility to neighbor consumption with the motivation of one’s own consumption, which gives rise to a menu of quantity premium or quantity discount. In the Erdös and Rényi graph (a special case of the social network model in this paper), they find that the pricing scheme does not screen network positions; consequently, the firm can offer a simple uniform price. The authors also find that, in the context of two-way connections, the firm-optimal consumption becomes linear in customer degree in the scale-free network.

scholarly journals Investigating the effectiveness of advertising on declining social networks

2014 ◽  
Vol 23 (1) ◽  
pp. 73-80
Author(s):  
GERGELY KOCSIS ◽  
◽  
IMRE VARGA ◽  
Keyword(s):  
Social Networks ◽  
Computer Simulations ◽  
Real Life ◽  
Simplified Model ◽  
The Real ◽  
Scale Free ◽  
Real Network ◽  
Information Spreading ◽  
Continuous Destruction

In this paper we aim to investigate the question if it’s worth it to advertise on declining social networks or not. Our investigations are based on computer simulations using a previously defined and here simplified model of information spreading. To make our results as close to the real life as possible we run simulations both on a real network sample and on several generated networks representing as well classical scale-free-like social topologies as declining social networks. As a result we found how the continuous destruction of the network affects the spreading, changing also the effectiveness of the advertising.

Information diffusion structure on social networks with general degree distribution

2021 ◽  
pp. 2150047
Author(s):  
Wenyao Li ◽  
Shuang Zhang ◽  
Wei Wang ◽  
Tao Lin
Keyword(s):  
Social Networks ◽  
Exponential Distribution ◽  
Infection Rate ◽  
Degree Distribution ◽  
Information Diffusion ◽  
Distance Distribution ◽  
Scale Free ◽  
Diffusion Structure ◽  
Network Scale ◽  
Main Factor

In this paper, we study the information diffusion structure on social networks with general degree distribution. To describe the information diffusion structure, we adopt six different viewpoints of metrics, including structural virality, distance variance, distance variability, distance susceptibility, cascade depth and cascade width. On Erdös–Rényi (ER) networks, we can intuitively see that as the diffusion tree becomes denser, the depth of the diffusion tree first increases to a peak and then decreases with the infection rate increasing, in addition the distance distribution of the diffusion tree obeys exponential distribution, and the metrics except cascade width decrease after reaching their peak values. When the information diffuses on scale-free (SF) networks, the diffusion trees are similar with the ones on ER networks. In other words, compared with the degree distribution, the infection rate is the main factor of diffusion tree in the same network scale.

The epidemic network construction and immunization based on node strength

2018 ◽  
Vol 32 (26) ◽  
pp. 1850319 ◽  
Author(s):  
Fuzhong Nian ◽  
Longjing Wang ◽  
Zhongkai Dang
Keyword(s):  
Social Networks ◽  
Real World ◽  
Scale Free Network ◽  
Network Construction ◽  
The Real ◽  
Scale Free ◽  
Social Ability ◽  
The Social ◽  
Free Network

In this paper, a new spreading network was constructed and the corresponding immunizations were proposed. The social ability of individuals in the real human social networks was reflected by the node strength. The negativity and positivity degrees were also introduced. And the edge weights were calculated by the negativity and positivity degrees, respectively. Based on these concepts, a new asymmetric edge weights scale-free network which was more close to the real world was established. The comparing experiments indicate that the proposed immunization is priority to the acquaintance immunization, and close to the target immunization.

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