Studying the co-evolution of information diffusion,vaccination behavior and disease transmission in multilayer networks with local and global effects

Today,with the rapid development of the internet,a large amount of information often accompanies the rapid transmission of disease outbreaks,and increasing numbers of scholars are studying the relationship between information and the disease transmission process using complex networks.In fact,the disease transmission process is very complex.Besides this information,there will often be individual behavioral measures and other factors to consider.Most of the previous research has aimed to establish a two-layer network model to consider the impact of information on the transmission process of disease,rarely divided into information and behavior,respectively.To carry out a more in-depth analysis of the disease transmission process and the intrinsic influencing mechanism,this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamic co-evolution of information diffusion,vaccination behavior,and disease transmission.This is achieved by considering four influential relationships between adjacent layers in multilayer networks.In the information layer,the diffusion process of negative information is described,and the feedback effects of local and global vaccination are considered.In the behavioral layer,an individual's vaccination behavior is described,and the probability of an individual receiving a vaccination is influenced by two factors:the influence of negative information,and the influence of local and global disease severity.In the disease layer,individual susceptibility is considered to be influenced by vaccination behavior.The state transition equations are derived using the micro Markov chain approach(MMCA),and disease prevalence thresholds are obtained.It is demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior,and is mainly influenced by global vaccination behavior;vaccination behavior is mainly influenced by local disease conditions,and is less influenced by global disease conditions;the disease transmission threshold increases with the increasing vaccination rate;and the scale of disease transmission increases with the increasing negative information diffusion rate and decreases with the increasing vaccination rate.Finally,it is found that when individual vaccination behavior considers both the influence of negative information and disease,it can increase the disease transmission threshold and reduce the scale of disease transmission.Therefore,we should resist the diffusion of negative information,increase vaccination proportions,and take appropriate protective measures in time.

Dynamics of information diffusion and disease transmission in time-varying multiplex networks with asymmetric activity levels

While the interaction between information and disease in static networks has been extensively investigated,many studies have ignored the characteristics of network evolution.In this study,we construct a new two-layer coupling model to explore the interactions between information and disease.The upper layer describes the diffusion of disease-related information,and the lower layer represents the disease transmission.We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation,revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network.Subsequently,we derive the disease outbreak threshold by using the microscopic Markov-chain approach(MMCA).Finally,we perform extensive Monte Carlo(MC)numerical simulations to verify the accuracy of our theoretical results.Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer.Moreover,reducing the damping factor can delay disease outbreaks and suppress disease transmission,while improving individual quarantine measures can contribute positively to disease control.This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.

Intervention against information diffusion in static and temporal coupling networks

Information diffusion in complex networks has become quite an active research topic.As an important part of this field,intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers.In particular,it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks.For this purpose,we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks.First,individual interactions are described by a modified activitydriven network(ADN)model.Then,we establish a novel node-based susceptible-infected-recovered-susceptible(SIRS)model to characterize the information diffusion dynamics.On these bases,three synergetic intervention strategies are formulated.Second,we derive the critical threshold of the controlled-SIRS system via stability analysis.Accordingly,we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget.Third,we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense,in which the optimal intervention inputs are obtained through optimal control theory and a forward-backward sweep algorithm.Finally,extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.

Subtle role of latency for information diffusion in online social networks

Information diffusion in online social networks is induced by the event of forwarding information for users, and latency exists widely in user spreading behaviors. Little work has been done to reveal the effect of latency on the diffusion process. In this paper, we propose a propagation model in which nodes may suspend their spreading actions for a waiting period of stochastic length. These latent nodes may recover their activity again. Meanwhile, the mechanism of forwarding information is also introduced into the diffusion model. Mean-field analysis and numerical simulations indicate that our model has three nontrivial results. First, the spreading threshold does not correlate with latency in neither homogeneous nor heterogeneous networks, but depends on the spreading and refractory parameter. Furthermore, latency affects the diffusion process and changes the infection scale. A large or small latency parameter leads to a larger final diffusion extent, but the intrinsic dynamics is different. Large latency implies forwarding information rapidly, while small latency prevents nodes from dropping out of interactions. In addition, the betweenness is a better descriptor to identify influential nodes in the model with latency, compared with the coreness and degree. These results are helpful in understanding some collective phenomena of the diffusion process and taking measures to restrain a rumor in social networks.

Bayesian Network Model of Product Information Diffusion and Reasoning of Influence

Information diffusion on social media has become a key strategy in people’s daily interactions. This paper studies consumers’ participation in the product information diffusion, and analyzes the complexity of information diffusion which is affected by many factors. Prior investigations of information diffusion have primarily focused on the composition of diffusion networks with independent factors and the intricacy of the process has not been completely evaluated. The majority of prior investigations have focused on strategies and the moving forces in social media processes and the determination of influential seed nodes, with few evaluations conducted about the factors affecting consumers’ choices in information diffusion. In this study, a Bayesian network model of product information diffusion was created to examine the links between factors and consumer deportment. It revealed how those factors had an impact on each other and on consumer deportment choice. The innovation of the thesis is reflected in the exploration and analysis of the specific communication path of product information diffusion, which provides a better marketing idea and practical method for the development of mobile e-commerce. The research findings can help identify the quantitative relationships between the factors affecting the process of product information diffusion and user behavior.

Impact of asymmetric activity on interactions between information diffusion and disease transmission in multiplex networks

Disease is a serious threat to human society.Understanding the characteristics of disease transmission is helpful for people to effectively control disease.In real life,it is natural to take various measures when people are aware of disease.In this paper,a novel coupled model considering asymmetric activity is proposed to describe the interactions between information diffusion and disease transmission in multiplex networks.Then,the critical threshold for disease transmission is derived by using the micro-Markov chain method.Finally,the theoretical results are verified by numerical simulations.The results show that reducing the activity level of individuals in the physical contact layer will have a continuous impact on reducing the disease outbreak threshold and suppressing the disease.In addition,the activity level of individuals in the virtual network has little impact on the transmission of the disease.Meanwhile,when individuals are aware of more disease-related information,the higher their awareness of prevention will be,which can effectively inhibit the transmission of disease.Our research results can provide a useful reference for the control of disease transmission.

Reshaping the urban hierarchy:patterns of information diffusion on social media

The spatial diffusion of information is a process governed by the flow of interpersonal communication.The emergence of the Internet and especially social media platforms has reshaped this process and previous research has studied how online social networks contribute to the diffusion of information.Understanding such processes can help devise methods to maximize or control the reach of information or even identify upcoming events and social movements.Yet activities in cyberspace are still confined to physical locations and this geographic connection tends to be overlooked.In this research,we focus on geographic regions instead of individuals and study how the underlying hierarchical structure of regions relates to their response to the information.We examined the top 30 populated cities and metropolitan areas in the U.S.and retrieved Twitter data related to two selected topics from these regions,the 2015 Nepal Earthquake and the#JesuisCharlie hashtag in response to the Paris attacks on the Charlie Hebdo offices.We analyzed the similarity among regions of their response using multiple statistical methods and three urban classifications.Our results indicate that the diffusion of information is impacted by the hierarchy of urban regions and that the Twitter responses act more similar when the populated regions are positioned at the same level in the urban hierarchy.

Communication Effect of Passengers on Information Diffusion in Metro Emergency

Information diffusion is significant for emergencymanagement as it can decide the severity of accidents. In this paper, we set up a communication model of passengers for the metroemergency. In the model, four categories of passengers are definedas unknown passengers, supportive passengers, neutral passengers and opposed passengers. Three passengers' characteristics are taken into account, such as spreading desire, the trustworthinessand the passengers' uncertainty about their opinions. From thesimulation results, we can see that the passengers' uncertainty about their opinions has a positive correlation with the time ofpassengers' opinions reaching consensus, while other two factorsboth have a negative correlation. The result is useful for metroofficials to guide and control emergency information.

Identifying Influential Communities Using IID for a Multilayer Networks

In online social networks(OSN),they generate several specific user activities daily,corresponding to the billions of data points shared.However,although users exhibit significant interest in social media,they are uninterested in the content,discussions,or opinions available on certain sites.Therefore,this study aims to identify influential communities and understand user behavior across networks in the information diffusion process.Social media platforms,such as Facebook and Twitter,extract data to analyze the information diffusion process,based on which they cascade information among the individuals in the network.Therefore,this study proposes an influential information diffusion model that identifies influential communities across these two social media sites.More-over,it addresses site migration by visualizing a set of overlapping communities using hyper-edge detection.Thus,the overlapping community structure is used to identify similar communities with identical user interests.Furthermore,the com-munity structure helps in determining the node activation and user influence from the information cascade model.Finally,the Fraction of Intra/Inter-Layer(FIL)dif-fusion score is used to evaluate the efficiency of the influential information diffu-sion model by analyzing the trending influential communities in a multilayer network.However,from the experimental result,it observes that the FIL diffusion score for the proposed method achieves better results in terms of accuracy,preci-sion,recall and efficiency of community detection than the existing methods.

Asynchronism of the spreading dynamics underlying the bursty pattern

The potential mechanisms of the spreading phenomena uncover the organizations and functions of various systems.However,due to the lack of valid data,most of early works are limited to the simulated process on model networks.In this paper,we track and analyze the propagation paths of real spreading events on two social networks:Twitter and Brightkite.The empirical analysis reveals that the spreading probability and the spreading velocity present the explosive growth within a short period,where the spreading probability measures the transferring likelihood between two neighboring nodes,and the spreading velocity is the growth rate of the information in the whole network.Besides,we observe the asynchronism between the spreading probability and the spreading velocity.To explain the interesting and abnormal issue,we introduce the time-varying spreading probability into the susceptible-infected(SI)and linear threshold(LT)models.Both the analytic and experimental results reproduce the spreading phenomenon in real networks,which deepens our understandings of spreading problems.

A study on the effects of diffusion of information on epidemic spread

In this work,the spread of a contagious disease on a society where the individuals may take precautions is modeled.The primary assumption is that the infected individuals transmit the infection to the susceptible members of the community through direct contact interactions.In the meantime,the susceptibles gather information from the adjacent sites which may lead to taking precautions.The SIR model is used for the diffusion of infection while the Bass equation models the information diffusion.The sociological classification of the individuals indicates that a small percentage of the population takes action immediately after being informed,while the majority expects to see some real advantage of taking action.The individuals are assumed to take two different precautions.The precursory measures are getting vaccinated or trying to avoid direct contact with the neighbors.A weighted average of states of the neighbors leads to the choice of action.The fully connected and scale-free Networks are employed as the underlying network of interactions.The comparison between the simple contagion diffusion and the diffusion of infection in a responsive society showed that a very limited precaution makes a considerable difference in the speed and the size of the spread of illness.Particularly,highly connected hub nodes play an essential role in the reduction of the spread of disease.

Collaborative Diffusion Model of Information and Behavior in Social Networks

Information diffusion may lead to behaviors related to information content.This paper considers the co-existence of information and behavior diffusion in social networks.The state of users is divided into six categories,and the rules and model of collaborative diffusion of information and behavior are established.The influence of different parameters and conditions on the proportions of behavior diffusion nodes and information diffusion ones is analyzed experimentally.The results show that the proportion of nodes taking action in uniform networks is higher than that in non-uniform networks.Although users are more likely to take actions related to information content after spreading or knowing information,the results show that it has little influence on the proportion of users taking action.The proportion is mainly affected by the probability that users who do not take action become ones who take.The greater the probability,the less the proportion of nodes who know information.In addition,compared with choosing the same node as the initial information and behavior diffusion node,choosing different nodes is more beneficial to the diffusion of behaviors related to information content.

Revisiting the death of geography in the era of Big Data:the friction of distance in cyberspace and real space

Many scholars have argued that the importance of geographic proximity in human interactions has been diminished by the use of the Internet,while others disagree with this argument.Studies have noted the distance decay effect in both cyberspace and real space,showing that interactions occur with an inverse relationship between the number of interactions and the distance between the locations of the interactors.However,these studies rarely provide strong evidence to show the influence of distance on interactions in cyberspace,nor do they quantify the differences in the amount of friction of distance between cyberspace and real space.To fill this gap,this study used massive amounts of social media data(Twitter)to compare the influence of distance decay on human interactions between cyberspace and real space in a quantitative manner.To estimate the distance decay effect in both cyberspace and real space,the distance decay function of interactions in each space was modeled.Estimating the distance decay in cyberspace in this study can help predict the degree of information flow across space through social media.Measuring how far ideas can be diffused through social media is useful for users of location-based services,policy advocates,public health officials,and political campaigners.

Assessment and Mapping of Potential Storm Surge Impacts on Global Population and Economy

With global climate change, population growth,and economic development in the twenty-first century,large cyclonic storm surges may result in devastating effects in some coastal areas of the world. However, due to the deficiency of global data and large-scale modeling efforts, the assessment and mapping of potential storm surge impacts at the global level are limited. In this article,the potential inundated area of global coastal zones is projected using information diffusion theory, based on the historical hourly sea-level observation records from the University of Hawaii Sea Level Center(UHSLC), considering variations in coastal morphology and tropical cyclone tracks. Combined with global demographic and GDP data,population and GDP at risk of storm surge impacts are calculated, mapped, and validated through the comparison with historical losses. The resulting potential impact maps provide a preliminary outlook on risks that may help governments of countries to make storm surge disaster prevention and reduction plans.

Propagation History Ranking in Social Networks:A Causality-Based Approach

Information diffusion is one of the most important issues in social network analysis.Unlike most existing works,which either rely on network topology or node profiles,this study focuses on the diffusion itself,i.e.,the recorded propagation histories.These histories are the evidence of diffusion and can be used to explain to users what happened in their networks.However,these histories can quickly grow in size and complexity,limiting their capacity to be intuitively understood.To reduce this information overload,in this paper we present the problem of propagation history ranking.The goal is to rank participant edges/nodes by their contribution to the diffusion.We first discuss and adapt a causal measure,Difference of Causal Effects(DCE),as the ranking criterion.Then,to avoid the complex calculation of DCE,we propose two integrated ranking strategies by adopting two indicators.One is responsibility,which captures the necessity aspect of causal effects.We further give an approximate algorithm,which could guarantee a feasible solution,for this indicator.The other is capability,which captures the sufficiency aspect of causal effects.Finally,promising experimental results are presented to verify the feasibility of the proposed ranking strategies.

Optimal control of alcoholism spreading through awareness over multiplex network

This paper proposes the SISRS epidemic model to represent alcohol addiction among people.The spreading of alcohol addiction is controlled by creating awareness among the people and also by treating them to overcome it.Multiplex network is used to study the dynamics of addiction.Alcoholism spreads over the physical contact layer and follows the SISRS process whereas human awareness spreads over the virtual contact layer and follows the UAU process.Based on the Microscopic Markov Chain Approach competing dynamics of spreading of alcohol addiction and human awareness diffusion are studied.Necessary conditions for the existence of an alcohol-free population are found.An optimal control problem using a suitable cost index is formulated to reduce the alcohol addicts and the optimal control strategy using Pontryagin’s Minimum Principle is determined.Numerical results are developed to find the effect of various parameters and to analyze the effects of different control strategies.The results obtained from this model are closer to the data collected in the National Survey of Drug Use and Health(NSDUH)from 2002 to 2018.

Investors’ Social Network and Return

Restricted by the availability of investors’account data,existing studies know little about the reasons for differences in investors’return in financial markets.Given this,this paper,based on the unique account data,reveals that the differences in investors’return are correlated to their locations in the social network.Conclusions are as follows.(1)Investors’social network constructed based on the submission time of completed orders describes the information diffusion process of financial markets.Information diffuses from the center of the network to the edge,and investors’return depends on their position in the network.(2)Investors’social network affects their return through the positive spillover mechanism of their behavior.Wealthy investors are in the center of the social network,the stronger the information sharing,the higher the status in the network,the higher the return;while retail investors are on the edge of the social network,and when their network centrality is certain,they even suffer return penalty for information sharing.(3)The speed of information diffusion in investors’social network has an important impact on asset pricing.Stocks’volatility,return,and liquidity are high in financial markets with an intermediate level of information diffusion speed.This paper puts forward new reasons for differences in investors’return from the perspective of investors’social network,and holds that big data in the capital market deserve further exploration with the social network method.