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.

General limited information diffusion method of small-sample information analysis in insurance

When analyzing and evaluating risks in insurance, people are often confronted with the situation of incomplete information and insufficient data, which is known as a small-sample problem. In this paper, a one-dimensional small-sample problem in insurance was investigated using the kernel density estimation method （KerM） and general limited information diffusion method （GIDM）. In particular, MacCormack technique was applied to get the solutions of GIDM equations and then the optimal diffusion solution was acquired based on the two optimization principles. Finally, the analysis introduced in this paper was verified by treating some examples and satisfying results were obtained.

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.

A comparative study of information diffusion in weblogs and microblogs based on social network analysis

Purpose: This paper intends to explore a quantitative method for investigating the characteristics of information diffusion through social media like weblogs and microblogs.By using the social network analysis methods,we attempt to analyze the different characteristics of information diffusion in weblogs and microblogs as well as the possible reasons of these differences.Design/methodology/approach: Using the social network analysis methods,this paper carries out an empirical study by taking the Chinese weblogs and microblogs in the field of Library and Information Science(LIS) as the research sample and employing measures such as network density,core/peripheral structure and centrality.Findings: Firstly,both bloggers and microbloggers maintain weak ties,and both of their social networks display a small-world effect. Secondly,compared with weblog users,microblog users are more interconnected,more equal and more capable of developing relationships with people outside their own social networks. Thirdly,the microblogging social network is more conducive to information diffusion than the blogging network,because of their differences in functions and the information flow mechanism. Finally,the communication mode emerged with microblogging,with the characteristics of micro-content,multi-channel information dissemination,dense and decentralized social network and content aggregation,will be one of the trends in the development of the information exchange platform in the future.Research limitations: The sample size needs to be increased so that samples are more representative. Errors may exist during the data collection. Moreover,the individual-level characteristics of the samples as well as the types of information exchanged need to be further studied.Practical implications: This preliminary study explores the characteristics of information diffusion in the network environment and verifies the feasibility of conducting a quantitative analysis of information diffusion through social media. In addition,it provides insight into the characteristics of information diffusion in weblogs and microblogs and the possible reasons of these differences.Originality/value: We have analyzed the characteristics of information diffusion in weblogs and microblogs by using the social network analysis methods. This research will be useful for a quantitative analysis of the underlying mechanisms of information flow through social media in the network environment.

Information diffusion on adaptive network

Based on the adaptive network, the feedback mechanism and interplay between the network topology and the diffusive process of information are studied. The results reveal that the adaptation of network topology can drive systems into the scale-free one with the assortative or disassortative degree correlations, and the hierarchical clustering. Meanwhile, the processes of the information diffusion are extremely speeded up by the adaptive changes of network topology.

Assessment of Agricultural Drought Risk in the Sichuan Basin Based on Information Diffusion Theory

Based on data of agricultural drought situation and sown area of main crops in each county or district of the Sichuan Basin, the spatial distribution and probability of agricultural drought risk at different risk levels were studied using normal information diffusion method, and the risk zoning was carried out. The results showed that normal information diffusion method could fit the distribution of agricultural drought risk in the Sichuan Basin. By comparison with the end of the 20^th century, agricultural drought risk in Meishan, Chongqing City and so on increased at the beginning of the 21^st century when x1≥ 10% or x1≥40%. Agricultural drought risk was low in the west of the Sichuan Basin, which was related to rich precipitation here, but it was high in Bazhong, Zhongjiang, Luxian and so forth. The risk zoning results can provide scientific references for disaster prevention and emergency management of government.

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.

Assessment of meteorological disasters based on information diffusion theory in Xinjiang, Northwest China

Data of flood, drought, hailstorms, and low temperature events in Xinjiang from 1949 to 2012 were analyzed with the diffusion method to assess the risk of the most common types of disasters in Xinjiang. It was proved that the frequency and intensity of meteorological disasters of the study area showed an increasing trend associated with global warming. Among the four types of disasters, surpass probability of drought was the largest, followed by hailstorm, low temperature and flood in turn. Moreover, the wavelet method analysis revealed that greater oscillations had occurred since 2000, which may be associated with the occurrence of extreme climatic changes. The spatial distribution of frequencies reveals that the northern slope of Tianshan Mountains is a multiple disaster area, the southern slope of Tianshan is the area where more floods and hailstorms occur, and the west of Turpan-Hami Basin is the area wind is prevalent. The relationships between disaster-affected areas and corresponding meteorological and socio-economic indexes were also analyzed. It indicated that there were significant positive correlations between the areas affected and the most meteorological and socio-economic indicators except the grain acreage.

Study on Information Diffusion Analysis in Social Networks and Its Applications

Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximiza- tion and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffu- sion analysis are listed for further study.

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.

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.

Understanding information interactions in diffusion： an evolutionary game-theoretic perspective

Social networks are fundamental media for dif- fusion of information and contagions appear at some node of the network and get propagated over the edges. Prior re- searches mainly focus on each contagion spreading indepen- dently, regardless of multiple contagions＇ interactions as they propagate at the same time. In the real world, simultaneous news and events usually have to compete for user＇s attention to get propagated. In some other cases, they can cooperate with each other and achieve more influences. In this paper, an evolutionary game theoretic framework is proposed to model the interactions among multiple con- tagions. The basic idea is that different contagions in social networks are similar to the multiple organisms in a popula- tion, and the diffusion process is as organisms interact and then evolve from one state to another. This framework statis- tically learns the payoffs as contagions interacting with each other and builds the payoff matrix. Since learning payoffs for all pairs of contagions IS almost impossible （quadratic in the number of contagions）, a contagion clustering method is proposed in order to decrease the number of parameters to fit, which makes our approach efficient and scalable. To ver- ify the proposed framework, we conduct experiments by us- ing real-world information spreading dataset of Digg. Exper- imental results show that the proposed game theoretic frame- work helps to comprehend the information diffusion process better and can predict users＇ forwarding behaviors with more accuracy than the previous studies. The analyses of evolution dynamics of contagions and evolutionarily stable strategy re- veal whether a contagion can be promoted or suppressed by others in the diffusion process.

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.

Behavior and Regularity Analysis of Online Topic Spreading

The spread of online topics,which is a complex socio-psychological and information dissemination process,can significantly influence the online public opinion. The behavior of online topics spreading is explored and its regularity is attempted to analyze. A general model for the spread of online topics is introduced,and the differential equation that describes the velocity of an online topic＇s spreading is derived. The velocity of an online topic＇s spread indicates the level of the topic＇s development and reflects its popularity over time. The proposed model has been theoretically analyzed and empirically studied,respectively. By analyzing the data set from a famous Internet forum,it is shown that the development of spread velocity of online topics has some certain features and our model matches the laws of reality. This method,which is suitable for forecasting the development trend of online topics’ spread velocity in short term,is also critical to the success of online topics’ regularity analysis.