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 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 Immunization of Cooperative Spreading Dynamics on Complex Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jun Wang ◽  
Shi-Min Cai ◽  
Tao Zhou
Keyword(s):  
Complex Networks ◽  
Numerical Simulations ◽  
Percolation Theory ◽  
Large Degree ◽  
Transmission Probability ◽  
Final State ◽  
The Monte Carlo Method ◽  
Spreading Dynamics ◽  
Opposite Situation ◽  
The Cost

Cooperative spreading dynamics on complex networks is a hot topic in the field of network science. In this paper, we propose a strategy to immunize some nodes based on their degrees. The immunized nodes disable the synergistic effect of cooperative spreading dynamics. We also develop a generalized percolation theory to study the final state of the spreading dynamics. By using the Monte Carlo method, numerical simulations reveal that immunizing nodes with a large degree cannot always be beneficial for containing cooperative spreading. For small values of transmission probability, immunizing hubs can suppress the spreading, while the opposite situation happens for large values of transmission probability. Furthermore, numerical simulations show that immunizing hubs increase the cost of the system. Finally, all numerical simulations can be well predicted by the generalized percolation theory.

scholarly journals Controlling Virus Spread Using an Intermittent Quarantine Strategy on Multiplex Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xingguo Li ◽  
Xiaoping Luo ◽  
Yiwu Wang
Keyword(s):  
Information Transmission ◽  
Time Delays ◽  
Information Diffusion ◽  
Mean Field ◽  
Transmission Probability ◽  
Virus Spread ◽  
Spreading Dynamics ◽  
Control Virus ◽  
Optimal Information ◽  
Virus Spreading

Virus spreading on the Internet will negatively affect cybersecurity. An intermittent quarantine immunization strategy to control virus spreading when containing information diffusion is proposed herein. In this model, information and virus spread on different subnetworks and interact with each other. We further develop a heterogeneous mean-field approach with time delays to investigate this model and use Monte Carlo simulations to systematically investigate the spreading dynamics. For a relatively short intermittent period, the optimal information transmission probability of the virus will be significantly suppressed. However, when the intermittent period is extremely long; increasing the probability of information transmission can control the virus spreading as well as suppress the increase in the intermittent period. Finally, it is shown that the average degree of the two subnetworks does not qualitatively affect the spreading dynamics.

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 Empirical Bayesian regression model for estimation of small rates

2012 ◽  
Vol 53 ◽  
Author(s):  
Gintautas Jakimauskas ◽  
Leonidas Sakalauskas
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Population Data ◽  
Real Data ◽  
Simulation Method ◽  
Bayesian Regression ◽  
Distribution Model ◽  
Estimation Model ◽  
Hospital Discharges ◽  
Large Populations

The efficiency of adding an auxiliary regression variable to the logit model in estimation of small probabilities in large populations is considered. Let us consider two models of distribution of unknown probabilities: the probabilities have gamma distribution (model (A)), or logits of the probabilities have Gaussian distribution (model (B)). In modification of model (B) we will use additional regression variable for Gaussian mean (model (BR)). We have selected real data from Database of Indicators of Statistics Lithuania – Working-age persons recognized as disabled for the first time by administrative territory, year 2010 (number of populations K = 60). Additionally, we have used average annual population data by administrative territory. The auxiliary regression variable was based on data – Number of hospital discharges by administrative territory, year 2010. We obtained initial parameters using simple iterative procedures for models (A), (B) and (BR). At the second stage we performed various tests using Monte-Carlo simulation (using models (A), (B) and (BR)). The main goal was to select an appropriate model and to propose some recommendations for using gamma and logit (with or without auxiliary regression variable) models for Bayesian estimation. The results show that a Monte Carlo simulation method enables us to determine which estimation model is preferable.

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 Synchronization and information transmission in spatio-temporal networks of deformable units

Pramana ◽  
2008 ◽  
Vol 70 (6) ◽  
pp. 1063-1076 ◽  
Author(s):  
F. M. Moukam Kakmeni ◽  
M. S. Baptista
Keyword(s):  
Information Transmission ◽  
Temporal Networks ◽  
Spatio Temporal

scholarly journals INTERPLAY BETWEEN HIV/AIDS EPIDEMICS AND DEMOGRAPHIC STRUCTURES BASED ON SEXUAL CONTACT NETWORKS

2007 ◽  
Vol 18 (06) ◽  
pp. 1025-1045 ◽  
Author(s):  
WEN-JIE BAI ◽  
TAO ZHOU ◽  
BING-HONG WANG
Keyword(s):  
Early Stage ◽  
Real Data ◽  
Risky Behaviors ◽  
Transmission Probability ◽  
Transmission Network ◽  
Power Law Distribution ◽  
Spreading Model ◽  
Antiretroviral Drug ◽  
Low Prevalence ◽  
Hiv Aids

In this article, we propose a network spreading model for HIV epidemics, wherein each individual is represented by a node of the transmission network and the edges are the connections between individuals along which the infection may spread. The sexual activity of each individual, measured by its degree, is not homogeneous but obeys a power-law distribution. Due to the heterogeneity of activity, the infection can persistently exist at a very low prevalence, which has been observed in the real data but cannot be illuminated by previous models with homogeneous mixing hypothesis. The model displays a clear picture of hierarchical spread: In the early stage the infection is adhered to these high-risk persons, and then, diffuses toward low-risk population. Furthermore, we find that to reduce the risky behaviors is much more effective in the fight against HIV/AIDS rather than the antiretroviral drug therapies. The prediction results show that the development of epidemics can be roughly categorized into three patterns for different countries, and the pattern of a given country is mainly determined by the average sex-activity and transmission probability per sexual partner. In most cases, the effect of HIV epidemics on demographic structure is very small. However, for some extremely countries, like Botswana, the number of sex-active people can be depressed to nearly a half by AIDS.

scholarly journals RAREsim: A simulation method for very rare genetic variants

2021 ◽  
Author(s):  
Megan Null ◽  
Josée Dupuis ◽  
Christopher R. Gignoux ◽  
Audrey E. Hendricks
Keyword(s):  
Rare Variant ◽  
Complex Traits ◽  
Rare Variants ◽  
Simulated Data ◽  
Real Data ◽  
Simulation Method ◽  
Sequencing Data ◽  
Variant Annotation ◽  
Causal Variants ◽  
Rare Genetic Variants

AbstractIdentification of rare variant associations is crucial to fully characterize the genetic architecture of complex traits and diseases. Essential in this process is the evaluation of novel methods in simulated data that mirrors the distribution of rare variants and haplotype structure in real data. Additionally, importing real variant annotation enables in silico comparison of methods that focus on putative causal variants, such as rare variant association tests, and polygenic scoring methods. Existing simulation methods are either unable to employ real variant annotation or severely under- or over-estimate the number of singletons and doubletons reducing the ability to generalize simulation results to real studies. We present RAREsim, a flexible and accurate rare variant simulation algorithm. Using parameters and haplotypes derived from real sequencing data, RAREsim efficiently simulates the expected variant distribution and enables real variant annotations. We highlight RAREsim’s utility across various genetic regions, sample sizes, ancestries, and variant classes.

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