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

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>

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.

Social networks currently serve as a primary platform for numerous connected intelligent individuals to share opinions and forward messages, which not only accelerate the diffusion of trending useful information but also facilitate the spreading of rumors and the execution of malicious attacks. Therefore, it is of crucial importance to understand how information diffuses in social networks. Most existing works either adopt empirical machine learning methods, which heavily rely on the quality and quantity of datasets, or develop mathematical dynamic models, which often neglect micro-interactions among users and the effects of time delays caused by users receiving information. In this paper, we propose an explainable framework to analyze information diffusion in social networks, which leverages the graphical evolutionary game theory to model scenarios where users receive and process information at varying rates. We define three types of time delay, model the micro-interaction among users, derive the dynamics of information diffusion, and analyze the conditions of system stability. We run simulations over both synthetic networks and three real-world social networks to validate the correctness of the proposed model. This work can provide a new perspective of modeling and predicting the information diffusion processes with time delays in different scenarios.

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.

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.

We study the problem of information dissemination in vehicular networks in this paper. Crucial information, such as traffic jamming status, is usually broadcasted inside the network. Each vehicle in the network has its own choice to either forward or drop packets for others. Although the energy consumption in general is not an issue in vehicular networks, forwarding every received packets is still not preferred as it may result in congestion in transmission. We model this problem via evolutionary game theory to investigate the cooperative behavior among vehicles. Simulation results show that, the cooperation ratio in the network is proportional to the setting of synergy factor for the evolutionary game. When nodes change their strategies according to the evolutionary game, information can be disseminated as fast as flooding scheme, while more bandwidth can be reserved.

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.

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

In social networks, studying rumor propagation patterns is essential for curbing the spread of rumors. Given the coexistence and conflict of multiple-type rumor information, as well as users’ cognitive differences, this article presents a rumor propagation model grounded in user cognition and evolutionary game theory. First, considering the potential impact of social relationships between users on rumor propagation, the KD-Tree algorithm is employed to uncover hidden connections between users, thereby enriching the topology of the user’s social network. Second, a user behavior driving mechanism for rumor, anti-rumor, and motivation-rumor types is constructed based on evolutionary games to reflect the interactive and strategic nature of users’ responses. Moreover, the Lotka-Volterra equation is utilized to explore the dynamic game of multi-type rumor information and the cognitive process of users. Finally, to address differences in users’ cognition, this article introduces the anti-rumor trust state A and the motivation-rumor trust state M , which arise from users’ exposure to multiple types of rumor information. Based on these trust states, a rumor propagation model, SIAMR, is constructed using user cognition and evolutionary game theory. Experiments demonstrate that the model accurately captures the dynamic interactions between multi-type rumor information and the transmission process of rumor topics in social networks. The proposed model integrates cognitive psychology with a strategic interaction framework, offering a more realistic representation of rumor propagation behavior in the real world. Experimental results reveal that SIAMR improves prediction accuracy by 14.23% over baseline models in simulating the dynamics of multiple types of rumors, effectively capturing users’ cognitive influences and the mechanisms of information competition.

The research on group behavior is effective for establishing a good network environment since people in social networks tend to form groups spontaneously. Most studies on group behavior on online social networks assume that all individuals are reduced to one cluster, ignoring the existence of potential clusters and their importance in group opinion dynamics. This article introduces a novel group-gravitational field (GGF) model to investigate the opinion evolution based on group behavior by the following aspects: 1) the GGF model reduces a cluster in the social network into a charge and the whole network into a gravitational field; 2) the GGF model calculates the initial influence of a cluster according to the topology information and further constructs a gravity matrix of the network based on the Coulomb law; and 3) opinion-leader clusters exert the internal field force on common opinion clusters inside the gravitational field. The GGF model simulates the evolution of opinions among clusters in a network and studies the law of group behavior according to the influence between clusters based on Coulomb’s law. Experiments on real social networks verify that the GGF model enhances the speed of opinion evolution significantly. The simulation experiments indicate that the existence of clusters promotes the rapid convergence of opinions, a gathering of followers influences information dissemination in social networks, and the GGF model fits the reality better. This article provides a new approach to network supervision and control.

Affinity Paths and information diffusion in social networks

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.

Modeling information diffusion on social networks can be used to guide the prediction and control of information propagation and improve the structure and functionality of social networks. Existing information diffusion prediction methods can predict information diffusion paths and its volume by modeling social network structure and user behavior. However, none of the existing methods take user activity level, which is proved to be critical in modeling the information diffusion process, into account, thus weaken the prediction accuracy. To solve this problem, this article proposes a Multi-Scale Activity Network (MSA-Net) to capture topological and historical affect features for different scales and to predict the users who will be affected at a specific future timestamp with the help of user activity level. Specifically, we first learn the network representation of three scales or levels: micro-scale, meso-scale, and macro-scale, which refers to the user level, intra-community level, and inter-community level, respectively. Then, we introduce the user activity level for each user by using user degree and average number of tweets per time unit to model the individual differences of users to achieve a more accurate prediction. Extensive experiments based on real-world datasets show that MSA-Net achieves a 6.14% improvement in terms of precision, a 6.74% improvement in terms of recall metrics, a 4.26% improvement in terms of F1-score, a 3.15% improvement in terms of MAP, and a 25.78% improvement in terms of NRMSE over the best existing baseline. The code and data are available at https://github.com/tsinghua-fib-lab/MSA-Net.