Information Propagation Model Research Articles

Source localization in signed networks based on dynamic message passing algorithm

Sign characteristics of social networks play a crucial role in the process of information dissemination, and structural balance theory is a significant property of signed networks. Despite some progress in source localization, the sign features of network structures are widely overlooked. Furthermore, the impact of structural balance on tracing remains unclear. In this paper, we explore a model of information propagation in signed networks and investigate source localization in the context of a given network state snapshot. We first propose a modified signed susceptibility-infection-recovery (S-SIR) propagation model, combining the positive/negative transmission rates based on structural balance theory, which is designed to better approximate the real-world social network structure. Next, a signed dynamic message passing algorithm (S-DMP) is proposed for source localization. Simulation experiments show that the S-DMP algorithm can fully utilize both the positive and negative attributes of the edges in the signed network and outperforms other methods in terms of source localization accuracy. These experiments prove that the proposed method can be used for source localization in the early stage of information transmission. Furthermore, we find that the accuracy of source localization is highest when there is a balance between the number of positive and negative edges.

Higher-order network information propagation model based on social impact theory

User relationships are becoming increasingly diverse in current online social networks. This paper utilizes simplicial complexes in higher-order networks to depict multiple user relationships and characterize the topology of social networks. By employing social impact theory and factors such as user interest and information aging to define state transition rules among users, we establish a UAPR information propagation model based on simplicial complexes to simulate the information propagation process in higher-order social networks. The proposed model is simulated on three types of synthetic networks and four real-world simplicial complexes, demonstrating its ability to accurately describe the dynamics and influences of information propagation. Furthermore, our results indicate that different network structures, user interest values, information aging speeds, and intimacy levels significantly influence the information transmission process.

Research on the characteristics of information propagation dynamic on the weighted multiplex Weibo networks

In order to simulate the forwarding situation of different categories of Weibo and discover interesting propagation phenomena in different layers of Weibo networks, this paper proposes the retweeting weighted multiplex networks and propagation model coupled with multi-class Weibo. Firstly, the weighted multiplex social network is constructed through the processing of Weibo network data. Secondly, a new information propagation model is established by using the weight and interlayer information of the Weibo multiplex network combined with the coupling factors in the propagation. Finally, the information propagation simulated by the propagation model is compared with the real data, so as to summarize different information propagation phenomena in multiplex social multiplex network. At the same time, by comparing the structure of the forwarding weighted multiplex network constructed by the short time data and the long time data, we find the self-similarity of the forwarding weighted multiplex network, which proves the generalization of the experiment. Through the above research, the mystery of the Weibo social network has been deeply explored, and a new perspective has been opened up for the exploration of social media information propagation.

Research on the Dynamics of Word-of-Mouth Influencing Stock Prices

Word-of-mouth (WOM) can be considered one form of public opinion, reflecting consumers’ views towards product or service quality. With the development of social media, WOM holds influence over investors and subsequently impacts the stock prices of companies. The purpose of this study is to uncover the dynamics encompassing the factors that influence WOM, its propagation mechanisms, and the consequential impact on reality illustrated through fluctuations in stock prices. The mathematical model to describe the whole process can be separated into two components: information propagation model and stock price fluctuation model. The cumulative abnormal return (CAR) is used to demonstrate fluctuations in stock prices. Utilizing a Lyapunov function, it is proven that the equilibrium point of the model is globally asymptotically stable. As for the real world, it suggests that WOM will eventually prevail online, leading to fluctuations in stock prices. Based on the event study method, the empirical examination of 43 real events shows that our model can effectively predict the CAR and reflect the influence on stock prices caused by WOM in real-world scenarios.

The social force model: a behavioral modeling approach for information propagation during significant events

Information diffusion otherwise known as the propagation, spread or dissemination of information occurs when a piece of information flows from a particular individual/community to another in a social network. Studies related to information propagation involve problems regarding the factors that affect the information propagation, how the information is disseminated, the speed of propagation, etc. Researchers have proposed information propagation models to understand the phenomenon and to answer these questions. These models have been effectively used in applications such as behavior analysis, public health care, etc. Although several studies are carried out in this field, the literature demands identifying the most influential factors of propagation in real time in cases of sudden unexpected significant disasters/epidemics/pandemics, since the existing propagation models seem unfit during such circumstances. In this paper, a novel information propagation model which predicts the top propagators of information related to a particular context is proposed. This model utilizes the past few weeks' data during a sudden outbreak of a disaster and identifies the most influential attributes of a user profile to predict the top propagators of the future. The proposed Social Force Model is inspired by a model used in studying the fear propagation pattern in pedestrian dynamics in real-life situation [Cornes FE, Frank GA, Dorso CO. Fear propagation and the evacuation dynamics. Simul Model Pract Theory. 2019;95:112–133.]. We have effectively mapped the various forces which constitute the Social Force Model such as the Desired Force, the Social Force and the Granular Force with respect to the online social network context in order to discover the key spreaders of information during a specific context. Apart from identifying the propagators, the proposed model discovers the key attributes by analyzing the behavior of users based on their past activities in the online social network.

Research on the dynamic spread of information in social networks based on relationship strength theory and feedback mechanism

Introduction: Studying the main factors and related paths of rumor propagation contributes to the precise governance of rumor information in social networks. Most existing network representation learning methods do not fit with real-world information propagation networks well, and the network cannot effectively model the temporal characteristics and dynamic evolution features of rumor information propagation.Methods: Our study proposes a new dynamic network representation model for information propagation. Additionally, the study introduces a feedback mechanism where the latest node representations are fed back to neighboring nodes.Results: The method solves the problem of delayed network representation and improves network representation performance.Discussion: We conducted experimental simulations, and the results indicate that a higher level of trust contributes to stable group relationships and converging opinions, reducing the likelihood of opinion dispersion. Furthermore, novelty of topics, and interactivity between users, and opinion leaders exhibit distinct characteristics in guiding public opinion. The viewpoint evolution of the newly constructed dynamic network representation model aligns more closely with viewpoint evolution in real-world social networks.

Multiview Clustering With Propagating Information Bottleneck.

In many practical applications, massive data are observed from multiple sources, each of which contains multiple cohesive views, called hierarchical multiview (HMV) data, such as image-text objects with different types of visual and textual features. Naturally, the inclusion of source and view relationships offers a comprehensive view of the input HMV data and achieves an informative and correct clustering result. However, most existing multiview clustering (MVC) methods can only process single-source data with multiple views or multisource data with single type of feature, failing to consider all the views across multiple sources. Observing the rich closely related multivariate (i.e., source and view) information and the potential dynamic information flow interacting among them, in this article, a general hierarchical information propagation model is first built to address the above challenging problem. It describes the process from optimal feature subspace learning (OFSL) of each source to final clustering structure learning (CSL). Then, a novel self-guided method named propagating information bottleneck (PIB) is proposed to realize the model. It works in a circulating propagation fashion, so that the resulting clustering structure obtained from the last iteration can "self-guide" the OFSL of each source, and the learned subspaces are in turn used to conduct the subsequent CSL. We theoretically analyze the relationship between the cluster structures learned in the CSL phase and the preservation of relevant information propagated from the OFSL phase. Finally, a two-step alternating optimization method is carefully designed for optimization. Experimental results on various datasets show the superiority of the proposed PIB method over several state-of-the-art methods.

Dynamics analysis and control of positive–negative information propagation model considering individual conformity psychology

Dynamics analysis and control of positive–negative information propagation model considering individual conformity psychology

Parameter identification method of information propagation models based on different network structures

In this paper, we create the rumor propagation model with diffusion behavior by considering the state of the rumor in both the time dimension and the space dimension comprehensively. Meanwhile, we demonstrate the reaction–diffusion model using Turing patterns after determining the prerequisites for their occurrence. In order to achieve the purpose of predicting and controlling rumors in time, we choose to utilize the parameter identification technique based on the Barzilai–Borwein (BB) algorithm and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. In the numerical simulation section, we first investigate how the rumor avoidance rate and cross-diffusion coefficients affect the propagation of rumors. Then, based on a continuous spatio-temporal system and complex network system, respectively, we perform parameter identification for the propagation model. We thoroughly examine how the type of algorithm, the quantity of unknown parameters, and the network structure affect the identification outcomes in terms of the cost function, error curve, and program function time. When the model constructed in this paper is used for parameter identification on different network structures, the error gap between the final value and the target value is not significant. However, the cost function and time consumption for parameter identification on complex networks are much smaller than on the continuous medium.

Dynamic analysis of the IEASWR information propagation model with Holling-type II functional response

Noting that the gap between individual communication abilities is amplified in the network era, we classify spreaders into super-spreaders and ordinary spreaders by the Pareto principle. In addition, when facing negative information, individuals with higher knowledge levels will recognize it. Based on these, in this study, an improved Ignorants-Hesitant-Super-spreaders-Ordinary spreaders-Wise-Removers (IEASWR) information propagation model was developed. Meanwhile, in view of the gradual saturation, density dependence and nonlinear relationship of the propagation process, the Holling-type II functional response function is introduced to information propagation modeling for the first time making it more realistic. First, the basic reproduction number [Formula: see text] is deduced. Then, the global asymptotic stability of the information-free/prevailing equilibria is proved by constructing the Lyapunov function. Furthermore, using the Pontryagin maximum principle, a real-time optimization guidance measure is proposed to achieve the correct guidance of negative information within the expected time. Finally, the validity of the theory is verified through numerical simulation.

Retracted: Research on Information Propagation Model in Social Network Based on BlockChain

Retracted: Research on Information Propagation Model in Social Network Based on BlockChain

Construction of the Information Dissemination Model and Calculation of User Influence Based on Attenuation Coefficient

Users’ online activities serve as a mirror, reflecting their unique personas, affiliations, interests, and hobbies within the real world. Network information dissemination is inherently targeted, as users actively seek information to facilitate precise and swift communication. Delving into the nuances of information propagation on the Internet holds immense potential for facilitating commercial endeavors such as targeted advertising, personalized product recommendations, and insightful consumer behavior analyses. Recognizing that the intensity of information transmission diminishes with the proliferation of competing messages, increased transmission distances, and the passage of time, this paper draws inspiration from the concept of heat attenuation to formulate an innovative information propagation model. This model simulates the “heat index” of each node in the transmission process, thereby capturing the dynamic nature of information flow. Extensive experiments, bolstered by comparative analyses of multiple datasets and relevant algorithms, validate the correctness, feasibility, and efficiency of our proposed algorithm. Notably, our approach demonstrates remarkable accuracy and stability, underscoring its potential for real‐world applications.

On the Group-Fairness-Aware Influence Maximization in Social Networks

The goal of influence maximization (IM) is to find a set of <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> nodes that maximize their influence spread over a social network. IM is a widely used model for information propagation in social networks. Although IM has been widely investigated in recent years, the fair propagation of information is still understudied. In this article, we consider the group-fairness-aware IM, where the objective is to ensure that information has been fairly distributed across different groups in the population. We extend the notion of group fairness in IM, introduced by Tsang <i>et al.</i>, by considering the speed of information propagation in different groups of a social network and formulating it as a new group-fairness metric. We propose a multiobjective metaheuristic (SetMOGWO), based on the multiobjective gray wolf optimizer, to improve the fair propagation of information in IM concerning various fairness metrics. Experimental results show that the proposed algorithm outperforms the previous work with respect to all introduced fairness metrics. We also carry out detailed experimental analyses on real-world networks and report interesting relationships between fairness concepts and network characteristics, including the price of ensuring fairness in social networks, the effect of social groups&#x2019; structure on fairness, and the dependence among fairness metrics. Especially, we show that traditional IM techniques often neglect smaller groups. On the other hand, we show that, with a low cost, it can be ensured that these groups receive a fair portion of resources in fair order.

Research on homogeneous information propagation in social network

In real network systems, there are numerous information propagation phenomena. However, the impact of homogeneity on information propagation is still not well understood. In this paper, we introduce a dynamic information propagation model named H-SIR. We also incorporate the propagation mechanism of content homogeneity and define the dynamic propagation rate based on users’ interest locking and fatigue level. Through simulation experiments, we demonstrate how homogeneous information spreads and evolves on the network. Furthermore, we compare the model with real data of COVID-19 information on the Sina-Weibo social platform and the traditional SIR model to jointly validate our model. Our results show that in social networks, if the themes of homogeneous information are diverse, even with an increasing amount of homogeneous information, it will hardly affect the propagation of this type of information. However, if users are recommended excessive homogeneous information, they will not focus entirely but instead display selective contact behavior. In the case of information fatigue, a type of information with a low degree of homogeneity will survive longer than a type of information with a high degree of homogeneity. Finally, we propose corresponding strategies to alleviate the phenomenon of information homogenization in social networks.

An Extension of the Susceptible–Infected Model and Its Application to the Analysis of Information Dissemination in Social Networks

Social media significantly influences business, politics, and society. Easy access and interaction among users allow information to spread rapidly across social networks. Understanding how information is disseminated through these new publishing methods is crucial for political and marketing purposes. However, modeling and predicting information diffusion is challenging due to the complex interactions between network users. This study proposes an analytical approach based on diffusion models to predict the number of social media users engaging in discussions on a topic. We develop a modified version of the susceptible–infected (SI) model that considers the heterogeneity of interactions between users in complex networks. Our model considers the network structure, abandons the assumption of homogeneous mixing, and focuses on information diffusion in scale-free networks. We provide explicit algorithms for modeling information propagation on different types of random graphs and real network structures. We compare our model with alternative approaches, both those considering network structure and those that do not. The accuracy of our model in predicting the number of informed nodes in simulated information diffusion networks demonstrates its effectiveness in describing and predicting information dissemination in social networks. This study highlights the potential of graph-based epidemic models in analyzing online discussion topics and understanding other phenomena spreading on social networks.

Dynamical behaviors and event-triggered impulsive control of a delayed information propagation model based on public sentiment and forced silence

Dynamical behaviors and event-triggered impulsive control of a delayed information propagation model based on public sentiment and forced silence

Competitive information propagation considering local-global prevalence on multi-layer interconnected networks

The popularity of online social networks (OSNs) promotes the co-propagation of multiple types of information. And there exist inevitably competitive interactions between these information, which will significantly affect the spreading trend of each information. Besides, the coupled topology of multi-layer interconnects exhibited in OSNs will also increase the research complexity of information propagation dynamics. To effectively address these challenges, we propose a novel competitive information propagation model on multi-layer interconnected networks, where the tendency of an individual to become a positive or negative spreader depends on the weighted consideration of local and global prevalence. Then the basic reproduction number is calculated via next-generation matrix method. And under the critical conditions of the basic reproduction number, the asymptotic stability of information-free and information-endemic equilibria is theoretically proven through Lyapunov stability theory. Besides, an optimal control problem involving two heterogeneous controls is formulated, aiming at achieving the best suppression performance of negative information with the minimum control cost. According to Cesari theorem and Pontryagin minimum principle, the existence and analytical formulation of optimal solutions are derived. Extensive numerical experiments are conducted to prove the correctness of our theoretical results, and evaluate the effectiveness of our proposed control strategies. This study can provide useful insights into the modeling and control of multiple information propagation considering multi-layer network topology and individual adaptive behavior.

SHI1I2R competitive information spreading model in online and offline two-layer networks in emergencies

Previous research on information propagation has primarily focused on either online social networks or offline social networks. Additionally, there remains a gap in understanding the propagation of competitive information in emergency scenarios. This paper introduces an SHI1I2R information spreading model to investigate the dynamics of competitive information dissemination and interactions in online and offline two-layer networks during emergencies. To enhance the accuracy of information propagation modeling, we depart from the conventional assumption of constant spreading and decay rates and instead propose nonlinear spreading and interest decay rates within the SHI1I2R model. Employing a microscopic Markov chain approach, we conduct theoretical analysis and derive the spreading threshold of information in two-layer networks. Subsequently, we perform numerical simulations to validate the effectiveness of the SHI1I2R model and explore the factors influencing information propagation. Our findings indicate that information spreads more rapidly and extensively in two-layer networks compared to one-layer networks. Furthermore, the final scale of information dissemination and the speed of propagation follow the order: random network > small-world network > scale-free network > regular network.

AN EVOLUTIONARY MODEL OF SOCIAL NETWORK STRUCTURE DRIVEN BY INFORMATION INTERACTION

The interaction of information and the evolution of network structure are inseparable. In order to construct social network evolution and information propagation models that better fit real-world scenarios, this paper proposes a social network structure evolution model driven by changes in the strength of relationships between individuals through their information interactions with each other. During the evolution process of the network, information interaction between individuals is also influenced by the network structure. Therefore, we improve traditional propagation models and construct an information propagation model with dynamic propagation rates. The proposed model is used to simulate both the spread of information and the evolution of network structures in real social networks. Finally, simulation results are compared to real-world data, demonstrating the effectiveness and rationality of the proposed model.

Dynamical behaviors of a delayed SIR information propagation model with forced silence function and control measures in complex networks.

Due to the advanced network technology, there is almost no barrier to information dissemination, which has led to the breeding of rumors. Intended to clarify the dynamic mechanism of rumor propagation, we formulate a SIR model with time delay, forced silence function and forgetting mechanism in both homogeneous and heterogeneous networks. In the homogeneous network model, we first prove the nonnegativity of the solutions. Based on the next-generation matrix, we calculate the basic reproduction number . Besides, we discuss the existence of equilibrium points. Next, by linearizing the system and constructing a Lyapunov function, the local and global asymptotically stability of the equilibrium points are found. In the heterogeneous network model, we derive the basic reproduction number through the analysis of a rumor-prevailing equilibrium point . Moreover, we conduct the local and global asymptotic stability analysis for the equilibrium points according to the LaSalle's Invariance Principle and stability theorem. As long as the maximum spread rate is large enough, the rumor-prevailing point is locally asymptotically stable when . Additionally, it hits that the system exists bifurcation behavior at due to the newly added forced silence function. Later, after adding two controllers to the system, we research the problem of optimal control. Finally, aimed at authenticating the above theoretical results, a serious of numerical simulation experiments are carried out.