scholarly journals Majority Vote in Social Networks: Make Random Friends or Be Stubborn to Overpower Elites

2021 ◽  
Author(s):  
Charlotte Out ◽  
Ahad N. Zehmakan
Keyword(s):  
Social Networks ◽  
Real World ◽  
Majority Vote ◽  
Graph Models ◽  
Stabilization Time ◽  
The Real ◽  
Random Graph Models ◽  
Small Set ◽  
High Degree ◽  
Majority Model

Consider a graph G, representing a social network. Assume that initially each node is colored either black or white, which corresponds to a positive or negative opinion regarding a consumer product or a technological innovation. In the majority model, in each round all nodes simultaneously update their color to the most frequent color among their connections. Experiments on the graph data from the real world social networks (SNs) suggest that if all nodes in an extremely small set of high-degree nodes, often referred to as the elites, agree on a color, that color becomes the dominant color at the end of the process. We propose two countermeasures that can be adopted by individual nodes relatively easily and guarantee that the elites will not have this disproportionate power to engineer the dominant output color. The first countermeasure essentially requires each node to make some new connections at random while the second one demands the nodes to be more reluctant towards changing their color (opinion). We verify their effectiveness and correctness both theoretically and experimentally. We also investigate the majority model and a variant of it when the initial coloring is random on the real world SNs and several random graph models. In particular, our results on the Erdős-Rényi, and regular random graphs confirm or support several theoretical findings or conjectures by the prior work regarding the threshold behavior of the process. Finally, we provide theoretical and experimental evidence for the existence of a poly-logarithmic bound on the expected stabilization time of the majority model.

Heterogeneity of social network based on degree sequence and community

2018 ◽  
Vol 32 (30) ◽  
pp. 1850331
Author(s):  
Xuequn Li ◽  
Shuming Zhou ◽  
Guanqin Lian ◽  
Gaolin Chen ◽  
Jiafei Liu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Degree Sequence ◽  
Community Based ◽  
The Real ◽  
Scale Free ◽  
Local Neighborhood ◽  
Low Degree ◽  
High Degree

As we know, the scale-free property of networks implies that there are a great deal of nodes with low degree and a few with high degree in networks. In this paper, we mainly stick to the analysis of the heterogeneity of social networks. Heterogeneity measure of degree sequence based on Laplacian centrality (HLC) and degree ratio (HDR) and local neighborhood (HLN) are presented. Furthermore, heterogeneities of community based on the size, edge abundant degree and density of community are explored, respectively. The heterogeneity measures of community are empirically analyzed in the real-world network in terms of these three indices, i.e., size, edge abundant degree and density of community.

scholarly journals The robustness of reciprocity: Experimental evidence that each form of reciprocity is robust to the presence of other forms of reciprocity

2020 ◽  
Vol 6 (23) ◽  
pp. eaba0504
Author(s):  
David Melamed ◽  
Brent Simpson ◽  
Jered Abernathy
Keyword(s):  
Social Networks ◽  
Prosocial Behavior ◽  
Experimental Evidence ◽  
Real World ◽  
Strong Support ◽  
Theoretical Models ◽  
Current Understanding ◽  
The Real ◽  
Topological Features

Prosocial behavior is paradoxical because it often entails a cost to one’s own welfare to benefit others. Theoretical models suggest that prosociality is driven by several forms of reciprocity. Although we know a great deal about how each of these forms operates in isolation, they are rarely isolated in the real world. Rather, the topological features of human social networks are such that people are often confronted with multiple types of reciprocity simultaneously. Does our current understanding of human prosociality break down if we account for the fact that the various forms of reciprocity tend to co-occur in nature? Results of a large experiment show that each basis of human reciprocity is remarkably robust to the presence of other bases. This lends strong support to existing models of prosociality and puts theory and research on firmer ground in explaining the high levels of prosociality observed in human social networks.

Measurement Error and Attenuation Bias in Exponential Random Graph Models

2016 ◽  
Vol 7 (1-2) ◽  
pp. 29-54
Author(s):  
Yeaji Kim ◽  
Leonardo Antenangeli ◽  
Justin Kirkland
Keyword(s):  
Social Networks ◽  
Random Graph ◽  
Measurement Errors ◽  
Asymptotic Properties ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
The Face ◽  
Distributed Measurement

AbstractExponential Random Graph Models (ERGMs) are becoming increasingly popular tools for estimating the properties of social networks across the social sciences. While the asymptotic properties of ERGMs are well understood, much less is known about how ERGMs perform in the face of violations of the assumptions that drive those asymptotic properties. Given that empirical social networks rarely meet the strenuous assumptions of the ERGM perfectly, practical researchers are often in the position of knowing their coefficients are imperfect, but not knowing precisely how wrong those coefficients may be. In this research, we examine one violation of the asymptotic assumptions of ERGMs – perfectly measured social networks. Using several Monte Carlo simulations, we demonstrate that even randomly distributed measurement errors in networks under study can cause considerable attenuation in coefficients from ERGMs, and do real harm to subsequent hypothesis tests.

scholarly journals Modularity based Community Detection in Social Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 564-569
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Research Community ◽  
Incorrect Result ◽  
The Real

The community detection is an interesting and highly focused area in the analysis of complex networks (CNA). It identifies closely connected clusters of nodes. In recent years, several approaches have been proposed for community detection and validation of the result. Community detection approaches that use modularity as a measure have given much weight-age by the research community. Various modularity based community detection approaches are given for different domains. The network in the real-world may be weighted, heterogeneous or dynamic. So, it is inappropriate to apply the same algorithm for all types of networks because it may generate incorrect result. Here, literature in the area of community detection and the result evaluation has been extended with an aim to identify various shortcomings. We think that the contribution of facts given in this paper can be very useful for further research.

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.

scholarly journals Humanoid environmental perception with Gaussian process regression

2016 ◽  
Vol 13 (6) ◽  
pp. 172988141666678
Author(s):  
Dingsheng Luo ◽  
Yaoxiang Ding ◽  
Xiaoqiang Han ◽  
Yang Ma ◽  
Yian Deng ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Real World ◽  
Environmental Changes ◽  
Sensory Information ◽  
Gaussian Process Regression ◽  
Environmental Perception ◽  
The Real ◽  
Regression Framework ◽  
High Level ◽  
High Degree

Nowadays, humanoids are increasingly expected acting in the real world to complete some high-level tasks humanly and intelligently. However, this is a hard issue due to that the real world is always extremely complicated and full of miscellaneous variations. As a consequence, for a real-world-acting robot, precisely perceiving the environmental changes might be an essential premise. Unlike human being, humanoid robot usually turns out to be with much less sensors to get enough information from the real world, which further leads the environmental perception problem to be more challenging. Although it can be tackled by establishing direct sensory mappings or adopting probabilistic filtering methods, the nonlinearity and uncertainty caused by both the complexity of the environment and the high degree of freedom of the robots will result in tough modeling difficulties. In our study, with the Gaussian process regression framework, an alternative learning approach to address such a modeling problem is proposed and discussed. Meanwhile, to debase the influence derived from limited sensors, the idea of fusing multiple sensory information is also involved. To evaluate the effectiveness, with two representative environment changing tasks, that is, suffering unknown external pushing and suddenly encountering sloped terrains, the proposed approach is applied to a humanoid, which is only equipped with a three-axis gyroscope and a three-axis accelerometer. Experimental results reveal that the proposed Gaussian process regression-based approach is effective in coping with the nonlinearity and uncertainty of the humanoid environmental perception problem. Further, a humanoid balancing controller is developed, which takes the output of the Gaussian process regression-based environmental perception as the seed to activate the corresponding balancing strategy. Both simulated and hardware experiments consistently show that our approach is valuable and leads to a good base for achieving a successful balancing controller for humanoid.

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