Dynamics of opinion formation with strengthen selection probability

2014 ◽  
Vol 25 (10) ◽  
pp. 1450050 ◽  
Author(s):  
Haifeng Zhang ◽  
Zhen Jin ◽  
Binghong Wang
Keyword(s):  
Complex Networks ◽  
Majority Rule ◽  
Probability Function ◽  
Adjustable Parameter ◽  
Opinion Formation ◽  
Selection Probability ◽  
Optimal Value ◽  
Linear Selection ◽  
Linear Probability ◽  
Regular Networks

The local majority rule is extensively accepted as a paradigmatic model to reflect the formation of opinion. In this paper, we study a model of opinion formation where opinion update rule is not based on the majority rule or linear selection probability but on a strengthen selection probability controlled by an adjustable parameter β. In particular, our proposed probability function can proximately fit the two extreme cases–linear probability function and majority rule or in between the two cases under different values of β. By studying such model on different kinds of networks, including different regular networks and complex networks, we find that there exists an optimal value of β giving the most efficient convergence to consensus regardless of the topology of networks. This work reveals that, compared with the majority rule and linear selection probability, the strengthen selection probability might be a more proper model in understanding the formation of opinions in society.

scholarly journals Estimating a resource selection function with line transect sampling

2002 ◽  
Vol 6 (4) ◽  
pp. 213-228 ◽  
Author(s):  
Bryan F. J. Manly
Keyword(s):  
Resource Selection ◽  
Probability Function ◽  
Resource Selection Function ◽  
Line Transect ◽  
Selection Function ◽  
Line Transect Sampling ◽  
Selection Probability ◽  
Transect Sampling ◽  
Conditional Maximum ◽  
Habitat Variables

A resource selection probability function is a function that gives the prob- ability that a resource unit (e.g., a plot of land) that is described by a set of habitat variables X1 to Xp will be used by an animal or group of animals in a certain period of time. The estimation of a resource selection function is usually based on the comparison of a sample of resource units used by an animal with a sample of the resource units that were available for use, with both samples being assumed to be effectively randomly selected from the relevant populations. In this paper the possibility of using a modified sampling scheme is examined, with the used units obtained by line transect sampling. A logistic regression type of model is proposed, with estimation by conditional maximum likelihood. A simulation study indicates that the proposed method should be useful in practice.

scholarly journals Dynamics of link states in complex networks: The case of a majority rule

2012 ◽  
Vol 86 (6) ◽  
Author(s):  
J. Fernández-Gracia ◽  
X. Castelló ◽  
V. M. Eguíluz ◽  
M. San Miguel
Keyword(s):  
Complex Networks ◽  
Majority Rule

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.

scholarly journals EIGENMODE OF THE DECISION-BY-MAJORITY PROCESS IN COMPLEX NETWORKS

2008 ◽  
Vol 11 (04) ◽  
pp. 565-579
Author(s):  
MAKOTO UCHIDA ◽  
SUSUMU SHIRAYAMA
Keyword(s):  
Complex Networks ◽  
Network Models ◽  
Ising Models ◽  
Final States ◽  
Zero Temperature ◽  
Opinion Formation ◽  
Initial State ◽  
Final State ◽  
Numerical Studies ◽  
Eigenmode Analysis

The nature of the dynamics of opinion formation or zero-temperature Ising models modeled as a decision-by-majority process in complex networks is investigated using eigenmode analysis. The Hamiltonian of the system is defined and estimated by eigenvectors of the adjacency matrix constructed from several network models. The rule of the process is assumed to be equivalent to the minimization of the Hamiltonian. The initial and final states of the dynamics are decomposed on the basis of the eigenvectors. The process and the eigenmodes are analyzed by numerical studies. We show that the magnitude of the coefficient for the largest eigenvector at the initial states is the key determinant for the resulting dynamics. We thus prove that the final state of the dynamics can be estimated by the eigenmodes of the initial state.

A Global Optimization Approach Based on Opinion Formation in Complex Networks

2019 ◽  
Vol 6 (3) ◽  
pp. 173-187 ◽  
Author(s):  
Homayoun Hamedmoghadam ◽  
Mahdi Jalili ◽  
Parham Moradi ◽  
Xinghuo Yu
Keyword(s):  
Global Optimization ◽  
Complex Networks ◽  
Optimization Approach ◽  
Opinion Formation

Complex networks

2018 ◽  
Author(s):  
Graziano Vernizzi ◽  
Henri Orland
Keyword(s):  
Complex Networks ◽  
Small World ◽  
Laplacian Matrix ◽  
Square Lattice ◽  
Small World Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Free Network ◽  
Scale Free Graphs ◽  
Regular Networks

This article deals with complex networks, and in particular small world and scale free networks. Various networks exhibit the small world phenomenon, including social networks and gene expression networks. The local ordering property of small world networks is typically associated with regular networks such as a 2D square lattice. The small world phenomenon can be observed in most scale free networks, but few small world networks are scale free. The article first provides a brief background on small world networks and two models of scale free graphs before describing the replica method and how it can be applied to calculate the spectral densities of the adjacency matrix and Laplacian matrix of a scale free network. It then shows how the effective medium approximation can be used to treat networks with finite mean degree and concludes with a discussion of the local properties of random matrices associated with complex networks.

Sign in / Sign up

Export Citation Format

Share Document