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 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.

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 Weighted Multiplex Social Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Xuzhen Zhu ◽  
Jinming Ma ◽  
Xin Su ◽  
Hui Tian ◽  
Wei Wang ◽  
...  
Keyword(s):  
Weight Distribution ◽  
Growth Pattern ◽  
Transmission Probability ◽  
Edge Weight ◽  
Multiplex Networks ◽  
Fixed Weight ◽  
Multiplex Network ◽  
Theoretical Predictions ◽  
Information Spreading ◽  
Initial Seed

Information spreading on multiplex networks has been investigated widely. For multiplex networks, the relations of each layer possess different extents of intimacy, which can be described as weighted multiplex networks. Nevertheless, the effect of weighted multiplex network structures on information spreading has not been analyzed comprehensively. We herein propose an information spreading model on a weighted multiplex network. Then, we develop an edge-weight-based compartmental theory to describe the spreading dynamics. We discover that under any adoption threshold of two subnetworks, reducing weight distribution heterogeneity does not alter the growth pattern of the final adoption size versus information transmission probability while accelerating information spreading. For fixed weight distribution, the growth pattern changes with the heterogeneous of degree distribution. There is a critical initial seed size, below which no global information outbreak can occur. Extensive numerical simulations affirm that the theoretical predictions agree well with the numerical results.

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.

scholarly journals Drawing transmission graphs for COVID-19 in the perspective of network science

2020 ◽  
Vol 148 ◽  
Author(s):  
N. Gürsakal ◽  
B. Batmaz ◽  
G. Aktuna
Keyword(s):  
Probability Distribution ◽  
Network Science ◽  
Pareto Distribution ◽  
Transmission Probability ◽  
Transmission Network ◽  
Pareto Principle ◽  
Infected People ◽  
Discrete Pareto Distribution ◽  
Local Transmission

Abstract When we consider a probability distribution about how many COVID-19-infected people will transmit the disease, two points become important. First, there could be super-spreaders in these distributions/networks and second, the Pareto principle could be valid in these distributions/networks regarding estimation that 20% of cases were responsible for 80% of local transmission. When we accept that these two points are valid, the distribution of transmission becomes a discrete Pareto distribution, which is a kind of power law. Having such a transmission distribution, then we can simulate COVID-19 networks and find super-spreaders using the centricity measurements in these networks. In this research, in the first we transformed a transmission distribution of statistics and epidemiology into a transmission network of network science and second we try to determine who the super-spreaders are by using this network and eigenvalue centrality measure. We underline that determination of transmission probability distribution is a very important point in the analysis of the epidemic and determining the precautions to be taken.

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

Reconstructing Diffusion Model for Virality Detection in News Spread Networks

2021 ◽  
pp. 98-111
Author(s):  
Kritika Jain ◽  
Ankit Garg ◽  
Somya Jain
Keyword(s):  
Diffusion Model ◽  
Experimental Analysis ◽  
Recommendation System ◽  
Continuous Process ◽  
The Internet ◽  
Temporal Network ◽  
Temporal Networks ◽  
Spreading Pattern ◽  
Effective Product ◽  
Business Technology

In today's competitive world, organizations take advantage of widely-available data to promote their products and increase their revenue. This is achieved by identifying the reader's preference for news genre and patterns in news spread network. Spreading news over the internet seems to be a continuous process which eventually triggers the evolution of temporal networks. This temporal network comprises of nodes and edges, where node corresponds to published articles and similar articles are connected via edges. The main focus of this article is to reconstruct a susceptible-infected (SI) diffusion model to discover the spreading pattern of news articles for virality detection. For experimental analysis, a dataset of news articles from four domains (business, technology, entertainment, and health) is considered and the articles' rate of diffusion is inferred and compared. This will help to build a recommendation system, i.e. recommending a particular domain for advertisement and marketing. Hence, it will assist to build strategies for effective product endorsement for sustainable profitability.

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