A mean-field-theoretic model for dual information propagation in networks

2019 ◽  
Vol 7 (4) ◽  
pp. 585-602
Author(s):  
Utkarsh Niranjan ◽  
Anurag Singh ◽  
Ramesh Kumar Agrawal
Keyword(s):  
Information Diffusion ◽  
Information Source ◽  
Mean Field ◽  
Spreading Rate ◽  
Theoretic Model ◽  
Scale Free ◽  
Final Population ◽  
Proposed Model ◽  
Free Network ◽  
Novel Model

Abstract The Internet is a place where a vast amount of information is flowing. With the deeper penetration of social media, everybody is participating in spreading information. Often we find ourselves confused with competing information on the same topic. In this work, we present a novel model for competitive information diffusion on the scale-free network. The proposed model is an extension of the classical DK model of rumour spreading. Most of previous competitive information diffusion models consider a different type of stiflers to be similar. In our model we have two separate compartments for different types of stiflers. We present a detailed analysis about the effect of infection rate on the prevalence of rumour in the network. To capture the large chunk of population one requires relatively higher spreading rate. Relative impact of spreading rate and stifler rate on the final population in different compartments is also presented. In our analysis, we find that if stifler rate is higher than the spreading rate, a large portion of population remains unaware of rumours. We also find that if the information source is a popular person than people have a bias towards that information and information coming from less popular persons lose its grip on the network and lose the competition. This analysis illustrates that why big companies hire famous celebrities to promote their products. We also demonstrate rumour spreading analysis with numerical solution, network simulation and real network topology of Facebook.

Identifying a Set of Key Members in Social Networks Using SDP-Based Stochastic Search and Integer Programming Algorithms

2017 ◽  
Vol 34 (03) ◽  
pp. 1750002 ◽  
Author(s):  
Wentao Wu ◽  
Wai Kin Victor Chan ◽  
Lei Chi ◽  
Zhiguo Gong
Keyword(s):  
Information Diffusion ◽  
Integer Program ◽  
Stochastic Search ◽  
Scale Free Network ◽  
Final Solution ◽  
Community Based ◽  
Scale Free ◽  
Free Network ◽  
Key Player Problem ◽  
Programming Algorithms

This paper presents two semi-definite programming (SDP) based methods to solve the Key Player Problem (KPP). The KPP is to identify a set of [Formula: see text] nodes (i.e., key players) from a social network of size [Formula: see text] such that the number of nodes connected to these [Formula: see text] nodes is maximized. The KPP has applications in social diffusion and products adoption as it helps maximizing information diffusion and impact. We first formulate the KPP as an integer program (IP) and then convert it into an SDP formulation, which can be solved efficiently and produce a set of high quality candidate solutions. We develop an IP-based algorithm and a stochastic search (greedy) algorithm to find the final solution for the KPP. We compare our algorithms with existing methods in small and large networks with different network structures, including random graph, scale-free network, and community-based scale-free network (CSN). Computational results show that our algorithms are more efficient in solving the KPP in all networks. In addition, we examine how the network structure influences the nodes coverage. It is found that CSNs allow the highest nodes coverage due to their community and scale-free structure.

scholarly journals A novel information diffusion model based on psychosocial factors with automatic parameter learning

2020 ◽  
pp. 50-50
Author(s):  
Sabina-Adriana Floria ◽  
Florin Leon
Keyword(s):  
Online Social Networks ◽  
Information Diffusion ◽  
Gregarious Behavior ◽  
Proposed Model ◽  
Transmission Of Information ◽  
Two Factors ◽  
Information Diffusion Model ◽  
Real World Datasets ◽  
Sociological Factor ◽  
Novel Model

Online social networks are the main choice of people to maintain their social relationships and share information or opinions. Estimating the actions of a user is not trivial because an individual can act spontaneously or be influenced by external factors. In this paper we propose a novel model for imitating the evolution of the information diffusion in a network as well as possible. Each individual is modeled as a node with two factors (psychological and sociological) that control its probabilistic transmission of information. The psychological factor refers to the node?s preference for the topic discussed, i.e. the information diffused. The sociological factor takes into account the influence of the neighbors? activity on the node, i.e. the gregarious behavior. A genetic algorithm is used to automatically tune the parameters of the model in order to fit the evolution of information diffusion observed in two real-world datasets with three topics. The reproduced diffusions show that the proposed model imitates the real diffusions very well.

scholarly journals Associated Credit Risk Contagion with Incubatory Period: A Network-Based Perspective

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kai Xu ◽  
Jianming Mo ◽  
Qian Qian ◽  
Fengying Zhang ◽  
Xiaofeng Xie ◽  
...  
Keyword(s):  
Field Theory ◽  
Credit Risk ◽  
Mean Field Theory ◽  
Mean Field ◽  
Stable State ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
The Mean ◽  
Credit Risk Contagion

Associated credit risk is a kind of credit risk among the associated credit entities formed by credit-related entities. Focusing on this hot topic of associated credit risk and the relevant contagion and considering the latent entities and their incubatory period, this paper builds an infectious dynamic model to describe the associated credit risk contagion of associated credit entities based on the mean-field theory of complex networks. Firstly, this paper analyzes the stable state of the associated credit risk contagion in the associated entity network, considering the latent entities and their incubatory period. Secondly, from the perspective of complex network and considering the incubatory period, a SHIS model is built to reveal how the incubatory period influences associated credit risk contagion. Finally, the sensitivity of some parameters is analyzed in the Barabási–Albert (BA) scale-free network. The results show the following: (i) the contagion threshold of associated credit risk is related to the incubatory period of latent entities, the recovery rate and infectivity of infected entities, and the newborn rate of credit entities; (ii) the infectious rate of infected entities, the mortality rate of credit entities, and the important factors stated in (i) are all significantly correlated with the density of infected entities.

An individual-based mean-field model for fake-news spreading on PSO-based networks

2020 ◽  
Vol 34 (19) ◽  
pp. 2050172
Author(s):  
Dongmei Fan ◽  
Jinling Wang
Keyword(s):  
Field Model ◽  
Mean Field ◽  
Spreading Rate ◽  
Critical Threshold ◽  
Influence Coefficient ◽  
Similarity Matrix ◽  
Maximum Eigenvalue ◽  
Fake News ◽  
Mean Field Model ◽  
Proposed Model

This paper proposes an individual-based mean-field model for fake news spreading on the PSO-based networks, assuming that ignorant individuals are more likely to believe and repost the fake news that are spread by the similar spreaders. Therefore, the spreading rate is related to the similarity between these individuals. Theoretical analysis shows that the critical threshold is in inverse proportion to both the maximum eigenvalue of the similarity matrix and the influence coefficient [Formula: see text]. Monte-Carlo simulations are performed on the PSO-based networks, demonstrating the effectiveness of the proposed model.

Rumor Propagation Model for Complex Network with Non-Uniform Propagation Rates

2014 ◽  
Vol 596 ◽  
pp. 868-872 ◽  
Author(s):  
Rui Sun ◽  
Wan Bo Luo
Keyword(s):  
Mean Field Theory ◽  
Mean Field ◽  
Propagation Rate ◽  
Propagation Model ◽  
Scale Free Network ◽  
Propagation Characteristics ◽  
Affecting Factors ◽  
Scale Free ◽  
Rumor Propagation ◽  
Free Network

Considering propagation characteristics and affecting factors of rumor in real-world complex networks, this paper described different propagation rates of different nodes by introducing the rumor acceptability function. Based on mean-field theory, this paper presented a rumor propagation model with non-uniform propagation rate, and then simulated the behaviour of rumor propagation on scale-free network and calculated the propagation thresholds by corresponding dynamics equation. Theoretical analysis and simulation results show that nodes with different rumor acceptability could lead to slowing the spread of rumors, make positive propagation threshold arise, and effectively contain the outbreak and reduce the risk of rumors.

Abnormal dynamics of cascading edge failures with congestion effect

2018 ◽  
Vol 29 (10) ◽  
pp. 1850095
Author(s):  
Jianwei Wang ◽  
Jialu He ◽  
Wei Chen ◽  
Bo Xu
Keyword(s):  
Scale Free Network ◽  
Ring Network ◽  
Scale Free ◽  
Congestion Effect ◽  
Free Network ◽  
Edge Based ◽  
Adjustable Weight ◽  
Novel Model ◽  
Real Traffic ◽  
Dynamical Change

Considering congestion effects in realistic network environments, we give a new method to adjust dynamically adjust the weight of the congested edge. We calculate the load on an edge based on the revised betweenness method and propose a novel model with three states of edges to investigate the dynamics of cascading failures in the ring network, the BA scale-free network, and the real traffic networks in London. By two robust metrics, we surprisingly observe the abnormal dynamics of cascading propagation, especially compared with that in the unadjustable weight, the curves of cascading dynamics in the adjustable weight are more irregular, which means that enhancing the capacity of each edge is not always better to avoid the cascading propagation. In addition, our simulation results show that the dynamical change of the edge’s weight makes the heterogeneous BA networks more vulnerable.

Analyses of Complex Networks Based on the Random Walk Model

2014 ◽  
Vol 610 ◽  
pp. 850-853
Author(s):  
Jing Wei Deng ◽  
Kai Ying Deng ◽  
Ying Xing Li
Keyword(s):  
Theoretical Analysis ◽  
Scale Free Network ◽  
Theoretical Justification ◽  
Scale Free ◽  
Proposed Model ◽  
Global Meaning ◽  
Master Equation Approach ◽  
Free Network ◽  
Free Behavior ◽  
Analytical Expressions

In this letter, we derive the analytical expressions of the degree distributions for a kind of networks model random initializing attractiveness and preferential linking, which analyzed degree evolution by using the master equation approach. We also discuss the theoretical justification of the scale-free behavior about the proposed model. The influencing range of initialization to the degree distribution only related to initialization’s expectation under the global meaning. Finally, a series of theoretical analysis and numerical simulations to the scale-free network model are conducted in this letter. The results of computer simulation is presented to the theoretical analysis.

A mixed SIR-SIS model to contain a virus spreading through networks with two degrees

2017 ◽  
Vol 28 (09) ◽  
pp. 1750114 ◽  
Author(s):  
Mohamed Essouifi ◽  
Abdelfattah Achahbar
Keyword(s):  
Sir Epidemic Model ◽  
Sis Model ◽  
Scale Free ◽  
Sir Epidemic ◽  
Proposed Model ◽  
Free Network ◽  
Viral Equilibrium ◽  
Infection Density ◽  
Model Computer ◽  
Virus Spreading

Due to the fact that the “nodes” and “links” of real networks are heterogeneous, to model computer viruses prevalence throughout the Internet, we borrow the idea of the reduced scale free network which was introduced recently. The purpose of this paper is to extend the previous deterministic two subchains of Susceptible-Infected-Susceptible (SIS) model into a mixed Susceptible-Infected-Recovered and Susceptible-Infected-Susceptible (SIR–SIS) model to contain the computer virus spreading over networks with two degrees. Moreover, we develop its stochastic counterpart. Due to the high protection and security taken for hubs class, we suggest to treat it by using SIR epidemic model rather than the SIS one. The analytical study reveals that the proposed model admits a stable viral equilibrium. Thus, it is shown numerically that the mean dynamic behavior of the stochastic model is in agreement with the deterministic one. Unlike the infection densities [Formula: see text] and [Formula: see text] which both tend to a viral equilibrium for both approaches as in the previous study, [Formula: see text] tends to the virus-free equilibrium. Furthermore, since a proportion of infectives are recovered, the global infection density [Formula: see text] is minimized. Therefore, the permanent presence of viruses in the network due to the lower-degree nodes class. Many suggestions are put forward for containing viruses propagation and minimizing their damages.

THE COMPLEX NETWORKS WITH RANDOM INITIALIZING AND PREFERENTIAL LINKING

2010 ◽  
Vol 24 (24) ◽  
pp. 4753-4759 ◽  
Author(s):  
TIELI SUN ◽  
JINGWEI DENG ◽  
KAIYING DENG ◽  
SHUANGLIANG TIAN
Keyword(s):  
Field Theory ◽  
Theoretical Analysis ◽  
Computer Simulations ◽  
Mean Field Theory ◽  
Mean Field ◽  
Scale Free ◽  
Proposed Model ◽  
Free Behavior ◽  
Analytical Expressions ◽  
Theoretical Results

In this paper, we first derive the analytical expressions of the degree distributions for the network with random initializing attractiveness and preferential linking by using the approach of mean-field theory. Then we discuss the justification of the scale-free behavior and give a remark about the proposed model. Finally, a series of theoretical analysis and numerical simulations for the network model are conducted. The computer simulations and the theoretical results are consistent, and display the effectiveness of the model.

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