GEGT-D: An Explainable Framework of Analyzing Information Diffusion in Social Network with Heterogeneous Time Delay

Social network is the mainstream medium of current information dissemination, and it is particularly important to accurately predict its propagation law. In this paper, we introduce a social network propagation model integrating multiple linear regression and infectious disease model. Firstly, we proposed the features that affect social network communication from three dimensions. Then, we predicted the node influence via multiple linear regression. Lastly, we used the node influence as the state transition of the infectious disease model to predict the trend of information dissemination in social networks. The experimental results on a real social network dataset showed that the prediction results of the model are consistent with the actual information dissemination trends.

The information dissemination in social networks is affected by many factors. However, node attitude which is an important influence factor of information dissemination in social network have not been fully considered in the previous works. Aiming at the problem of the influence of node attitude on information dissemination, this paper proposes an information propagation model based on evolutionary game. Firstly, from the individual point of view, the node’s attitude update rules are defined according to non-Bayesian social learning rules. Secondly, an inter-node game matrix based on attitude value is established. Based on the evolution analysis paradigm, an information dissemination model based on node attitude is established. The equilibrium solution of dynamic equations is replicated for both positive and negative attitudes, and the corresponding equilibrium points are stabilized. The validity of the proposed model is verified by numerical analysis and simulation experiments. They all show that the different attitudes of nodes play an important role in information dissemination.

<span lang="EN-US">Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks.</span>

Online social networks have recently become an innovative and effective method for spreading information among people around the world. Information diffusion, rumour spreading and diseases infection are all instances of stochastic processes that occur over the edges of social networks. Many prior works have carried out empirical studies and diffusion models to understand how information propagates in online social networks; however they suffer from problems. In this paper, we propose an information diffusion model inspired by information propagation among people. Our proposed Social Behavioural Information Diffusion Model, abbreviated as SBIDM, considers the effect of mainstream media like TV and radio, as well as interaction with the neighbours. The advantages of our approach are four-fold. First, it models information diffusion in social networks inspired by social life, which considers the effect of aggregate social behaviour to diffuse information; second, it allows partial knowledge to be held in each individual; third, it considers the effects of social media in propagating information as well as the effects of interacting with neighbours; and last but not least, it is applicable to different types of data including synthetic and well-known real social networks like Facebook, Amazon, Epinions and DBLP. To explore the advantages of our approach, many experiments with different settings and specifications were conducted. The obtained results are very promising.

Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media

A novel negative feedback information dissemination model based on online social network

Social networks are ubiquitous, which has been one of the important topics of many scholars. With the rapid development of information technology in recent years, people's social platforms are not limited to offline, and online social networks have gradually become an indispensable part of our lives. However, there is still a lack of research on the differences between online and offline social networks. Therefore, this paper probes into the differences between online and offline social network information dissemination by collecting data from online and offline double-layer coupled networks, and comparing and analyzing the data. Research shows that there are some differences between online and offline social networks in the law of information dissemination, which will have a certain impact on real life. Therefore, three suggestions are put forward: 1. At the initial stage of promotion, there are few audience groups, so offline methods can be adopted. With the expansion of information dissemination scope, gradually turning to online promotion, the best promotion effect will be achieved at the fastest speed. 2. The speed and effect of information dissemination can't be ignored for people who have no value in social network information dissemination. 3. Based on the complexity of the public opinion information dissemination network, users need to constantly cultivate their ability to identify information, the management of major social platforms earnestly perform their regulatory responsibilities, and the relevant functional departments of the government gradually establish a public opinion guidance mechanism. If joint efforts can be formed, the public opinion prevention and control effect will be more significant.

Social hotspot propagation dynamics model based on multidimensional attributes and evolutionary games

Affinity Paths and information diffusion in social networks

The issue of the speed of information dissemination in the environment of social networks is considered. A conceptual model is proposed based on Monod equation, where biomass growth rate depends on the concentration of the substrate. A parallel between the main data of microbiological processes and processes in social networks is drawn. The major factors influencing information dissemination speed are described. The favorable factors are demonstrated, which, like substrate, enhance the speed of growth, and after saturation slow it down or stop completely. Following a similar model of phytoplankton reproduction (Jorgensen equation) there have been identified three elements that can limit its speed, similarly, in social networks they are — the concentration of the audience with a high level of information starvation, audience capacity, and volume of the attention resource. This study is useful for viral marketing theory, will enable to generate forecasts and manage decisions.

In social networks, as popular content reshares from user to user, information flows in the form of cascades. Modeling such information diffusion in social networks is of great importance and has attracted tremendous amounts of research interest. While existing work extensively studies open social networks as exemplified by Twitter and Facebook, information cascade in diffusion-restricted social networks remains much less understood. To tackle the problem above, in this paper we empirically study the embedded webpages (a.k.a. H5 webpages) spreading in WeChat Moments (WM) - a diffusion-restricted social network based on the largest Chinese social platform WeChat. WM differs from open social networks as it pays more attention to users' privacy (e.g., users' information can only be seen by their friends). In particular, we systematically analyze rich information cascades of advertising and political webpages in WM from a diffusion perspective, and further predict the growth of information cascades. We demonstrate that it is feasible to predict the scale of cascades at an early stage, and there exists a strong correlation between the cascade scale and cascade structure. This result suggests that a better understanding of information diffusion in WM is very critical, which can be further exploited to many applications such as viral marketing advertising and rumor detection.

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.

Online social media has emerged as a critical factor in information dissemination, search, marketing, expertise and influence discovery, and potentially an important tool for mobilizing people. It has also given researchers access to massive quantities of social data for empirical analysis. These data sets offer a rich source of evidence for studying dynamics of individual and group behavior, the structure of networks and global patterns of the flow of information on them. However, in most previous studies, the structure of the underlying networks was not directly visible but had to be inferred from the flow of information from one individual to another. As a result, we do not yet understand dynamics of information spread on networks or how the structure of the network affects it. We analyze data from two popular social news sites, Digg and Twitter, to understand the mechanisms of information diffusion in social networks, in the process, uncovering the primary role played by individual’s cognitive constraints in online social behavior.

Modeling the dynamism of HIV information diffusion in multiplex networks of homeless youth

The process of rumor spreading among people can be represented as information diffusion in social network. The scale of rumor spread changes greatly depending on starting vertices. If we can select vertices that contribute to large scale diffusion, the vertices are expected to be important for viral marketing. Given a network and the size of the starting vertices, the problem of selecting vertices for maximizing information diffusion is called as influence maximization problem. We propose three new approximation methods for influence maximization problem in dynamic networks. These methods are the extensions of previous methods for static networks to dynamic networks. Experiments for comparing the performance show that our proposed method achieves 1.5 times as much in the scale of diffusion and approximately 7.8 times faster in computational time at the maximum compared with previous heuristic methods.