Information spreading dynamics in hypernetworks

2018 ◽  
Vol 495 ◽  
pp. 475-487 ◽  
Author(s):  
Qi Suo ◽  
Jin-Li Guo ◽  
Ai-Zhong Shen
Keyword(s):  
Spreading Dynamics ◽  
Information Spreading

scholarly journals Identifying the Influential Latent Edges for Promoting the Co-SIR Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dan Yang ◽  
Liming Pan ◽  
Zhidan Zhao ◽  
Tao Zhou
Keyword(s):  
Real World ◽  
Message Passing ◽  
Sir Model ◽  
Research Area ◽  
Disease Outbreak ◽  
Limited Attention ◽  
Original Network ◽  
Spreading Model ◽  
Spreading Dynamics ◽  
Information Spreading

The network-based cooperative information spreading is a widely existing phenomenon in the real world. For instance, the spreading of disease outbreak news and disease prevention information often coexist and interact with each other on the Internet. Promoting the cooperative spreading of information in network-based systems is a subject of great importance in both theoretical and practical perspectives. However, very limited attention has been paid to this specific research area so far. In this study, we propose an effective approach for identifying the influential latent edges (that is, the edges that do not originally exist) which, if added to the original network, can promote the cooperative susceptible-infected-recovered (co-SIR) dynamics. To be specific, we first obtain the probabilities of each nodes being in different node states by the message-passing approach. Then, based on the state probabilities of nodes obtained, we come up with an indicator, which incorporates both the information of network topology and the co-SIR dynamics, to measure the influence of each latent edge in promoting the co-SIR dynamics. Thus, the most influential latent edges can be located after ranking all the latent edges according to their quantified influence. We verify the rationality and superiority of the proposed indicator in identifying the influential latent edges of both synthetic and real-world networks by extensive numerical simulations. This study provides an effective approach to identify the influential latent edges for promoting the network-based co-SIR information spreading model and offers inspirations for further research on intervening the cooperative spreading dynamics from the perspective of performing network structural perturbations.

scholarly journals Information Spreading on Memory Activity-Driven Temporal Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Linfeng Zhong ◽  
Yu Bai ◽  
Changjiang Liu ◽  
Juan Du ◽  
Weijun Pan
Keyword(s):  
Information Transmission ◽  
Memory Effect ◽  
Real Data ◽  
Simulation Method ◽  
Transmission Probability ◽  
Temporal Networks ◽  
Spreading Dynamics ◽  
Opposite Situation ◽  
Information Spreading ◽  
Markovian Approach

Information spreading dynamics on temporal networks have attracted significant attention in the field of network science. Extensive real-data analyses revealed that network memory widely exists in the temporal network. This paper proposes a mathematical model to describe the information spreading dynamics with the network memory effect. We develop a Markovian approach to describe the model. Using the Monte Carlo simulation method, we find that network memory may suppress and promote the information spreading dynamics, which depends on the degree heterogeneity and fraction of bigots. The network memory effect suppresses the information spreading for small information transmission probability. The opposite situation happens for large value of information transmission probability. Moreover, network memory effect may benefit the information spreading, which depends on the degree heterogeneity of the activity-driven network. Our results presented in this paper help us understand the spreading dynamics on temporal networks.

Role of lurkers in threshold-driven information spreading dynamics

2021 ◽  
Vol 104 (3) ◽  
Author(s):  
Zhongyuan Ruan ◽  
Bin Yu ◽  
Xiyun Zhang ◽  
Qi Xuan
Keyword(s):  
Spreading Dynamics ◽  
Information Spreading

scholarly journals Information Spreading on Activity-Driven Temporal Networks with Two-Step Memory

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Linfeng Zhong ◽  
Xiaoyu Xue ◽  
Yu Bai ◽  
Jin Huang ◽  
Qing Cheng ◽  
...  
Keyword(s):  
Network Science ◽  
Critical Mass ◽  
Transmission Probability ◽  
Temporal Network ◽  
Temporal Networks ◽  
Spreading Model ◽  
Spreading Dynamics ◽  
Information Spreading ◽  
Initial Seed ◽  
Markovian Approach

Information spreading dynamics on the temporal network is a hot topic in the field of network science. In this paper, we propose an information spreading model on an activity-driven temporal network, in which a node is accepting the information dependents on the cumulatively received pieces of information in its recent two steps. With a generalized Markovian approach, we analyzed the information spreading size, and revealed that network temporality might suppress or promote the information spreading, which is determined by the information transmission probability. Besides, the system exists a critical mass, below which the information cannot globally outbreak, and above which the information outbreak size does not change with the initial seed size. Our theory can qualitatively well predict the numerical simulations.

scholarly journals Virus-Information Coevolution Spreading Dynamics on Multiplex Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jian Wang ◽  
Xiaolin Qin ◽  
Hongying Fang
Keyword(s):  
Field Theory ◽  
Mathematical Model ◽  
Numerical Simulations ◽  
Systematic Study ◽  
Dynamic Process ◽  
Mean Field Theory ◽  
Mean Field ◽  
Multiplex Networks ◽  
Spreading Dynamics ◽  
Information Spreading

Virus and information spreading dynamics widely exist in complex systems. However, systematic study still lacks for the interacting spreading dynamics between the two types of dynamics. This paper proposes a mathematical model on multiplex networks, which considers the heterogeneous susceptibility and infectivity in two subnetworks. By using a heterogeneous mean-field theory, we studied the dynamic process and outbreak threshold of the system. Through extensive numerical simulations on artificial networks, we find that the virus’s spreading dynamics can be suppressed by increasing the information spreading probability, decreasing the protection power, or decreasing the susceptibility and infectivity.

scholarly journals Financial Risk Information Spreading on Metapopulation Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Min Lin ◽  
Li Duan
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Financial Risk ◽  
Transmission Probability ◽  
Risk Information ◽  
Discrete Time Markov Chain ◽  
Markov Chain Approach ◽  
Spreading Dynamics ◽  
Information Spreading ◽  
And Migration

The financial risk information diffuses through various kinds of social networks, such as Twitter and Facebook. Individuals transmit the financial risk information which can migrate among different platforms or forums. In this paper, we propose a financial risk information spreading model on metapopulation networks. The subpopulation represents a platform or forum, and individuals migrate among them to transmit the information. We use a discrete-time Markov chain approach to describe the spreading dynamics’ evolution and deduce the outbreak threshold point. We perform numerical simulation on artificial networks and discover that the financial risk information can be promoted once increasing the information transmission probability and active subpopulation fraction. The weight variance and migration probability cannot significantly affect the financial risk spreading size. The discrete-time Markov chain approach can reasonably predict the above phenomena.

scholarly journals Competing Complex Information Spreading in Multiplex Social Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiang Li ◽  
Bocheng Hou
Keyword(s):  
Field Theory ◽  
Mathematical Model ◽  
Network Science ◽  
Mean Field Theory ◽  
Mean Field ◽  
Global Information ◽  
Multiplex Networks ◽  
Complex Information ◽  
Spreading Dynamics ◽  
Information Spreading

Coevolution spreading dynamics on complex networks is a hot topic, which attracts much attention in network science. This paper proposes a mathematical model to describe the two competing complex information spreading dynamics on multiplex networks. An individual can only accept one of the two pieces of information. A heterogeneous mean-field theory is developed to describe the spreading dynamics. We reveal different regions through Monte Carlo simulations of the competing complex information spreading dynamics: no global information, one information dominant, and two information coexistence. We finally find that the heterogeneity of the multiplex networks’ degree distributions does not qualitatively affect the results.

scholarly journals Dynamics analysis of an online gambling spreading model on scale-free networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yu Kong ◽  
Tao Li ◽  
Yuanmei Wang ◽  
Xinming Cheng ◽  
He Wang ◽  
...  
Keyword(s):  
Network Topology ◽  
Negative Impact ◽  
Online Gambling ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Scale Free ◽  
Spreading Model ◽  
Spreading Dynamics ◽  
Scale Free Networks ◽  
Gambling Policy

AbstractNowadays, online gambling has a great negative impact on the society. In order to study the effect of people’s psychological factors, anti-gambling policy, and social network topology on online gambling dynamics, a new SHGD (susceptible–hesitator–gambler–disclaimer) online gambling spreading model is proposed on scale-free networks. The spreading dynamics of online gambling is studied. The basic reproductive number $R_{0}$ R 0 is got and analyzed. The basic reproductive number $R_{0}$ R 0 is related to anti-gambling policy and the network topology. Then, gambling-free equilibrium $E_{0}$ E 0 and gambling-prevailing equilibrium $E_{ +} $ E + are obtained. The global stability of $E_{0}$ E 0 is analyzed. The global attractivity of $E_{ +} $ E + and the persistence of online gambling phenomenon are studied. Finally, the theoretical results are verified by some simulations.

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