Information Propagation In Social Networks Research Articles

DMHANT: DropMessage Hypergraph Attention Network for Information Propagation Prediction.

Predicting propagation cascades is crucial for understanding information propagation in social networks. Existing methods always focus on structure or order of infected users in a single cascade sequence, ignoring the global dependencies of cascades and users, which is insufficient to characterize their dynamic interaction preferences. Moreover, existing methods are poor at addressing the problem of model robustness. To address these issues, we propose a predication model named DropMessage Hypergraph Attention Networks, which constructs a hypergraph based on the cascade sequence. Specifically, to dynamically obtain user preferences, we divide the diffusion hypergraph into multiple subgraphs according to the time stamps, develop hypergraph attention networks to explicitly learn complete interactions, and adopt a gated fusion strategy to connect them for user cascade prediction. In addition, a new drop immediately method DropMessage is added to increase the robustness of the model. Experimental results on three real-world datasets indicate that proposed model significantly outperforms the most advanced information propagation prediction model in both MAP@k and Hits@K metrics, and the experiment also proves that the model achieves more significant prediction performance than the existing model under data perturbation.

Influence maximization based on discrete particle swarm optimization on multilayer network

Influence maximization (IM) aims to strategically select influential users to maximize information propagation in social networks. Most of the existing studies focus on IM in single-layer networks. However, we have observed that individuals often engage in multiple social platforms to fulfill various social needs. To make better use of this observation, we consider an extended problem of how to maximize influence spread in multilayer networks. The Multilayer Influence Maximization (MLIM) problem is different from the IM problem because information propagation behaves differently in multilayer networks compared to single-layer networks: users influenced on one layer may trigger the propagation of information on another layer. Our work successfully models the information propagation process as a Multilayer Independent Cascade model in multilayer networks. Based on the characteristics of this model, we introduce an approximation function called Multilayer Expected Diffusion Value (MLEDV) for it. However, the NP-hardness of the MLIM problem has posed significant challenges to our work. To tackle the issue, we devise a novel algorithm based on Discrete Particle Swarm Optimization. Our algorithm consists of two stages: 1) the candidate node selection, where we devise a novel centrality metric called Random connectivity Centrality to select candidate nodes, which assesses the importance of nodes from a connectivity perspective. 2)the seed selection, where we employ a discrete particle swarm algorithm to find seed nodes from the candidate nodes. We use MLEDV as a fitness function to measure the spreading power of candidate solutions in our algorithm. Additionally, we introduce a Neighborhood Optimization strategy to increase the convergence of the algorithm. We conduct experiments on four real-world networks and two self-built networks and demonstrate that our algorithms are effective for the MLIM problem.

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.

Social Bots and Information Propagation in Social Networks: Simulating Cooperative and Competitive Interaction Dynamics

With the acceleration of human society’s digitization and the application of innovative technologies to emerging media, popular social media platforms are inundated by fresh news and multimedia content from multiple more or less reliable sources. This abundance of circulating and accessible information and content has intensified the difficulty of separating good, real, and true information from bad, false, and fake information. As it has been proven, most unwanted content is created automatically using bots (automated accounts supported by artificial intelligence), and it is difficult for authorities and respective media platforms to combat the proliferation of such malicious, pervasive, and artificially intelligent entities. In this article, we propose using automated account (bots)-originating content to compete with and reduce the speed of propagating a harmful rumor on a given social media platform by modeling the underlying relationship between the circulating contents when they are related to the same topic and present relative interest for respective online communities using differential equations and dynamical systems. We studied the proposed model qualitatively and quantitatively and found that peaceful coexistence could be obtained under certain conditions, and improving the controlled social bot’s content attractiveness and visibility has a significant impact on the long-term behavior of the system depending on the control parameters.

A New BAT and PageRank Algorithm for Propagation Probability in Social Networks

Social networks have increasingly become important and popular in modern times. Moreover, the influence of social networks plays a vital role in various organizations, including government organizations, academic research organizations and corporate organizations. Therefore, strategizing the optimal propagation strategy in social networks has also become more important. Increasing the precision of evaluating the propagation probability of social networks can indirectly influence the investment of cost, manpower and time for information propagation to achieve the best return. This study proposes a new algorithm, which includes a scale-free network, Barabási–Albert model, binary-addition tree (BAT) algorithm, PageRank algorithm, Personalized PageRank algorithm and a new BAT algorithm to calculate the propagation probability of social networks. The results obtained after implementing the simulation experiment of social network models show that the studied model and the proposed algorithm provide an effective method to increase the efficiency of information propagation in social networks. In this way, the maximum propagation efficiency is achieved with the minimum investment.

A policy-aware epistemic framework for social networks

Abstract We provide a semantic framework to specify information propagation in social networks; our semantic framework features both the operational description of information propagation and the epistemic aspects in social networks. In our framework, based on annotated labelled transition systems, actions are decorated with function views to specify different types of announcements. Our function views enforce various common types of local privacy policies, i.e. those policies concerning a single action. Furthermore, we specify global privacy policies, those concerning multiple actions, using a combination of modal $\mu $-calculus and epistemic logic. To illustrate the applicability of our framework, we apply it to the specification of a real-world case study. As a fundamental property for the epistemic aspect of our semantic model, we prove that its indistinguishability relations are equivalence relations, namely they are reflexive, symmetric and transitive. We also study the complexity bounds for the model-checking problem concerning a subset of our logic and show that model checking is PSPACE-complete for the studied subset.

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

The growing popularity of Online Social Networks (OSN) have prompted an increasing number of companies to promote their brands and products through social media. This paper presents a topological potential scheme for predicting influential nodes from large scale OSNs to support more intelligent brand communication. We first construct a weighted network model for the users and their relationships extracted from the brand-related content in OSNs. We quantitatively measure the individual value of the nodes from both the network structure and brand engagement aspects. Moreover, we have addressed the problem of influence decay along with information propagation in social networks and use the topological potential theory to evaluate the importance of the nodes by their individual values as well as the individual values of their surrounding nodes. The experimental results have shown that the proposed method is able to predict influential nodes in large-scale OSNs. We investigate the top-k influential nodes identified by our method in detail, which are quite different from those identified by using pure network structure or individual value. We can obtain an identification result with a higher ratio of verified users and user coverage by using our method compared to existing typical approaches.

Locating multiple information sources in social networks based on the naming game

Identifying the source of information in a network plays a key role in controlling the impact of information. Herein, we study the problem of multiple source localization in the context of information propagation in social networks. We use the theory of the naming game to conduct observations. Moreover, we divide the observations into different sets based on the information provided by them and then estimate the source of each set. Finally, we combine the source of each observation set to obtain all the estimated information sources. The proposed method can locate sources without knowing the number of information sources. Simulations on four real data sets are provided to verify the performance of our method.

A trust model for spreading gossip in social networks: a multi-type bootstrap percolation model.

We introduce here a multi-type bootstrap percolation model, which we call -Bootstrap Percolation ( -BP), and apply it to study information propagation in social networks. In this model, a social network is represented by a graph G whose vertices have different labels corresponding to the type of role the person plays in the network (e.g. a student, an educator etc.). Once an initial set of vertices of G is randomly selected to be carrying a gossip (e.g. to be infected), the gossip propagates to a new vertex provided it is transmitted by a minimum threshold of vertices with different labels. By considering random graphs, which have been shown to closely represent social networks, we study different properties of the -BP model through numerical simulations, and describe its implications when applied to rumour spread, fake news and marketing strategies.

TSSCM: A synergism-based three-step cascade model for influence maximization on large-scale social networks.

Identification of the most influential spreaders that maximize information propagation in social networks is a classic optimization problem, called the influence maximization (IM) problem. A reasonable diffusion model that can accurately simulate information propagation in social networks is the key step to efficiently solving the IM problem. Synergism of neighbor nodes plays an important role in information propagation dynamics. Some known diffusion models have considered the reinforcement mechanism in defining the activation threshold. Most of these models focus on the synergetic effects of nodes on their common neighbors, but the accumulation of synergism has been neglected in previous studies. Inspired by these facts, we first discuss the catalytic role of synergism in the spreading dynamics of social networks and then propose a novel diffusion model called the synergism-based three-step cascade model (TSSCM) based on the above analysis and the three-degree influence theory. Finally, we devise an algorithm for solving the IM problem based on the TSSCM. Experiments on five real large-scale social networks demonstrate the efficacy of our method, which achieves competitive results in terms of influence spreading compared to the four other algorithms tested.

Uncertainty-based False Information Propagation in Social Networks

Many network scientists have investigated the problem of mitigating or removing false information propagated in social networks. False information falls into two broad categories: disinformation and misinformation. Disinformation represents false information that is knowingly shared and distributed with malicious intent. Misinformation in contrast is false information shared unwittingly, without any malicious intent. Many existing methods to mitigate or remove false information in networks concentrate on methods to find a set of seeding nodes (or agents) based on their network characteristics (e.g., centrality features) to treat. The aim of these methods is to disseminate correct information in the most efficient way. However, little work has focused on the role of uncertainty as a factor in the formulation of agents’ opinions. Uncertainty-aware agents can form different opinions and eventual beliefs about true or false information resulting in different patterns of information diffusion in networks. In this work, we leverage an opinion model, called Subjective Logic (SL), which explicitly deals with a level of uncertainty in an opinion where the opinion is defined as a combination of belief, disbelief, and uncertainty, and the level of uncertainty is easily interpreted as a person’s confidence in the given belief or disbelief. However, SL considers the dimension of uncertainty only derived from a lack of information (i.e., ignorance), not from other causes, such as conflicting evidence. In the era of Big Data, where we are flooded with information, conflicting information can increase uncertainty (or ambiguity) and have a greater effect on opinions than a lack of information (or ignorance). To enhance the capability of SL to deal with ambiguity as well as ignorance, we propose an SL-based opinion model that includes a level of uncertainty derived from both causes. By developing a variant of the Susceptible-Infected-Recovered epidemic model that can change an agent’s status based on the state of their opinions, we capture the evolution of agents’ opinions over time. We present an analysis and discussion of critical changes in network outcomes under varying values of key design parameters, including the frequency ratio of true or false information propagation, centrality metrics used for selecting seeding false informers and true informers, an opinion decay factor, the degree of agents’ prior belief, and the percentage of true informers. We validated our proposed opinion model using both the synthetic network environments and realistic network environments considering a real network topology, user behaviors, and the quality of news articles. The proposed agent’s opinion model and corresponding strategies to deal with false information can be applicable to combat the spread of fake news in various social media platforms (e.g., Facebook).

GDTM: A Gaussian Dynamic Topic Model for Forwarding Prediction Under Complex Mechanisms

Forwarding is the keyway for information propagation in social networks that affected by complex factors. Due to the fact that the forwarding mechanisms are essentially unclear, this paper focuses on the formation and evolution of the external as well as internal driving mechanisms and develops a Gaussian dynamic topic model for forwarding prediction by incorporating all the information of nodes and edges. First, based on the diversity of communities each user located in, latent Dirichlet allocation (LDA) traditional text modeling method is applied to user following relationships modeling that leads to the initial formation of communities, and user interacting relationships modeling that leads to the evolution of communities. Taking the advantage of LDA topic model in dealing with the problem of polysemy and synonym, we can mine user latent distribution over communities and analyze the external community driving effects. Second, considering the differences in individual habits, time factor is introduced and the dynamic topic model is proposed to model user behavioral attributes. Meanwhile, in regard to continuous user attributes modeling, the parameter of topic-word distribution in the topic model is replaced by multivariate Gaussian distributions, which shown to be effective at capturing the regularities of individual behavioral habits and analyzing the internal individual driving effects. Finally, combining with external and internal factors, a probabilistic graph model is used to modeling forwarding behavior. We propose methods based on Gibbs sampling and an expectation–maximization algorithm to estimate model parameters and fit our model to predict user forwarding actions. Experimental results indicate that the model can not only detect the latent communities but also can improve the performance of forwarding prediction effectively.

Partial differential equation modeling with Dirichlet boundary conditions on social networks

The being a wide range of applications of the Internet, social networks have become an effective and convenient platform for information communication, propagation and diffusion. Most of information exchange and spreading exist in social networks. The issue of information diffusion in social networks is getting more and more attention by government and individuals. The researchers investigated either empirical studies or focused on ordinary differential equation (ODE) models with only consideration of temporal dimension in most prior work. As is well known, partial differential equations (PDEs) can describe temporal and spatial patterns of information diffusion over online social networks; however, until now, results for understanding information propagation of social networks over both temporal and spatial dimensions are few. This paper is devoted to investigating a non-autonomous diffusive logistic model with Dirichlet boundary conditions to describe the process of information propagation in social networks. By constructing upper and lower solutions we obtain the dynamic behavior of the solution to the non-autonomous diffusive logistic model. Our results show that information diffusion is greatly affected by the diffusion coefficient d(t) and the intrinsic growth rate r(t).

Improved SIR Advertising Spreading Model and Its Effectiveness in Social Network

The traditional SIR model cannot fully reflect the regularity of information propagation in social networks. In this paper, the advertising spreading model which is applied to social networks is established, and the corresponding dynamic evolution equations are given. Meanwhile, due to that there is no unified evaluation criteria for the validity of spreading models currently, the application of AEI, the advertising effectiveness index to evaluate and analyze the effectiveness of spreading models is put forward in this paper. The experiment results demonstrate that the model proposed in this paper can correctly reflect the trend of advertising spreading in social network, and accurately describe the spreading process, and the validity of the model is also verified in this paper.

Information Propagation in Social Networks with Overlapping Community Structure

Information Propagation in Social Networks with Overlapping Community Structure

Price Shock Detection With an Influence-Based Model of Social Attention

There has been increasing interest in exploring the impact of human behavior on financial market dynamics. One of the important related questions is whether attention from society can lead to significant stock price movements or even abnormal returns. To answer the question, we develop a new measurement of social attention, named periodic cumulative degree of social attention , by simultaneously considering the individual influence and the information propagation in social networks. Based on the vast social network data, we evaluate the new attention measurement by testing its significance in explaining future abnormal returns. In addition, we test the forecasting ability of social attention for stock price shocks, defined by the cumulative abnormal returns. Our results provide significant evidence to support the intercorrelated relationship between the social attention and future abnormal returns. The outperformance of the new approach in predicting price shocks is also confirmed by comparison with several benchmark methods.

Minimizing the Influence Propagation in Social Networks for Linear Threshold Models

Innovation or information propagation in social networks has been widely studied in recent years. Most of the previous works are focused on solving the problem of influence maximization, which aims to identify a small subset of early adopters in a social network to maximize the influence propagation under a given diffusion model. In this paper, motivated by practical scenarios, we propose two different influence minimization problems. We consider a Linear Threshold diffusion model and provide a general solution to the first problem solving a linear integer programming. For the second problem, we provide a technique to search for an optimal solution that works only in particular cases and discuss a simple heuristic to find a solution in the general case.

Effective spreading from multiple leaders identified by percolation in the susceptible-infected-recovered (SIR) model

Social networks constitute a new platform for information propagation, but its success is crucially dependent on the choice of spreaders who initiate the spreading of information. In this paper, we remove edges in a network at random and the network segments into isolated clusters. The most important nodes in each cluster then form a set of influential spreaders, such that news propagating from them would lead to extensive coverage and minimal redundancy. The method utilizes the similarities between the segmented networks before percolation and the coverage of information propagation in each social cluster to obtain a set of distributed and coordinated spreaders. Our tests of implementing the susceptible-infected-recovered model on Facebook and Enron email networks show that this method outperforms conventional centrality-based methods in terms of spreadability and coverage redundancy. The suggested way of identifying influential spreaders thus sheds light on a new paradigm of information propagation in social networks.

Impact of Neighbors on the Privacy of Individuals in Online Social Networks

Abstract The problem of user privacy enforcement in online social networks (OSN) cannot be ignored and, in recent years, Facebook and other providers have improved considerably their privacy protection tools. However, in OSN’s the most powerful data protection “weapons” are the users themselves. The behavior of an individual acting in an OSN highly depends on her level of privacy attitude: an aware user tends not to share her private information, or the private information of her friends, while an unaware user could not recognize some information as private, and could share it without care to her contacts. In this paper, we experimentally study the role of the attitude on privacy of an individual and her friends on information propagation in social networks. We model information diffusion by means of an extension of the Susceptible-Infectious-Recovered (SIR) epidemic model that takes into account the privacy attitude of users. We employ this diffusion model in stochastic simulations on a synthetic social network, designed for miming the characteristics of the Facebook social graph.