Dynamics Of Information Propagation Research Articles

Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network.

Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using transfer entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons.

Coupled dynamics of information propagation and emotion influence: Emerging emotion clusters for public health emergency messages on the Chinese Sina Microblog

A major distinction between the COVID-19 pandemic and previous pandemics is the significant role of social media platforms in shaping public adherence to non-pharmaceutical interventions and vaccine acceptance. The information propagation and individual emotion communication relevant to public health messages have been promoted in the Chinese Sina Microblog, one of the most popular social platforms in China, through two major interaction mechanisms, “commenting” and “forwarding”. Here, we develop a hybrid system of macro information propagation dynamics Susceptible-Commenting-Forwarding-Immune model coupled with the micro emotion influence dynamics Hegselmann-Krause model, termed HK-SCFI coupled system, and reveal the mechanisms of individual emotion infection using the commenting and forwarding data (including time point and content) publicly available on public opinion events in the Chinese Sina Microblog. We demonstrate how our data-driven and model-informed analysis can provide quantitative insights into the co-evolution mechanism of individual emotion during information propagation, describe the emergency of emotion clusters, and predict individual emotion evolution trends. Our study shows that multiple interactive mechanisms can facilitate the propagation of public messages and shape public sentiment through online social networks. Our modeling framework can potentially be a tool for designing effective propagation strategies during public health emergencies.

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.

Modeling the dynamics of information propagation in the temporal and spatial environment

In this paper, we try to establish a non-smooth susceptible–infected–recovered (SIR) rumor propagation model based on time and space dimensions. First of all, we prove the existence and uniqueness of the solution. Secondly, we divide the system into two parts and discuss the existence of equilibrium points for each of them. For the left part, we define R 0 to study the relationship between R 0 and the existence of equilibrium points. For the right part, we classify many different cases by discussing the coefficients of the equilibrium point equation. Then, on this basis, we perform a bifurcation analysis of the non-spatial system and find conditions that lead to the existence of saddle-node bifurcation. Further, we consider the effect of diffusion. We specifically analyze the stability of equilibrium points. In addition, we analyze the Turing instability and Hopf bifurcation occurring at some equilibrium points. According to the Lyapunov number, we also determine the direction of the bifurcation. When I = I c , we discuss conditions for the existence of discontinuous Hopf bifurcation. Finally, through numerical simulations and combined with the practical meaning of the parameters, we prove the correctness of the previous theoretical theorem.

Modeling and quantifying the influence of rumor and counter-rumor on information propagation dynamics

Today, the spread of Internet rumors has become increasingly easier, however, once they spread widely, the network environment and even the social order will be broken down. Therefore, to help in designing effective rumor control strategies, we propose the two-stage rumor propagation dynamics model, in which the susceptible/educated-infected-recovered (SO-S/EIR) dynamics model is used to characterize the first stage and the susceptible/educated-infected-denied-recovered (C-S/EIDR) dynamics model is to characterize the second stage. In our model, according to the difference of rumor spreaders' confidence in the rumor, we divide them into super-infected and normal-infected, moreover, the global model incorporates vigilantes and rumor debunkers to reflect the diversity of public opinions. Our model is based on the forwarded quantity in the Chinese Sina-microblog and takes into account the impact of interactions between forwarding users, own cognitions, and own knowledge level on the propagation of the rumor. Our Model-based analytic and numerical analyses show that forwarding and transfer probabilities in our model are in accordance with the actual accident situation, besides, our sensitivity analyses describe important factors that affect the spread of the rumor in support of decision strategies to refute the rumor.

Modeling and quantifying the influence of opinion involving opinion leaders on delayed information propagation dynamics

In this letter, an opinion-delay susceptible-forwarding-immunized model (OD-SFI) is proposed to investigate the influence of opinion involving opinion leaders, expressed in two pieces of information posted online subsequently, on the propagation dynamics of both information. The model describes the evolution of forwarding quantities, introduces homogeneity and/or heterogeneity index of opinions, and describes how these measures impact the propagation efficacy. Data fitting using the real data of Chinese Sina-microblog can accurately parameterize the model, quantify the opinion mutual influence; and sensitivity analyses also suggest strategies for effective information dissemination.

Spectator‐filter‐spreader‐stifler information propagation model with wake‐up mechanism in social networks

SummaryNowadays, social network platforms play a significant role in daily life, allowing people to hear what is going on around the world without leaving home. The research on the internal transformation of information propagation is critical, which can help restrain the propagation of fake information or rumors and promote the widespread dissemination of effective information. To better understand the transition of states in information propagation, this article considers a new filter state that does not transmit information temporarily and establishes a spectator‐filter‐spreader‐stifler (SFIR) model. Mainly, to explore the dynamics of information propagation, the article introduces two different wake‐up mechanisms including the spontaneous awakening of the filter and the infection of the leader spreaders to the passive stifler, which relies on the proportion of current spreaders as well as the doubt degree on information. Afterward, based on the average field equations built, the nonpropagation equilibrium point E0 and propagation equilibrium point E1 are obtained by theoretical analysis. Moreover, the simulation results reveal that spontaneous wake‐up mechanism accelerates the information propagation while leader spreaders infection increases the scope of information propagation, which can provide theoretical support for controlling information propagation more effectively.

Quantify the role of superspreaders -opinion leaders- on COVID-19 information propagation in the Chinese Sina-microblog.

BackgroudEffective communication of accurate information through social media constitutes an important component of public health interventions in modern time, when traditional public health approaches such as contact tracing, quarantine and isolation are among the few options for the containing the disease spread in the population. The success of control of COVID-19 outbreak started from Wuhan, the capital city of Hubei Province of China relies heavily on the resilience of residents to follow public health interventions which induce substantial interruption of social-economic activities, and evidence shows that opinion leaders have been playing significant roles in the propagation of epidemic information and public health policy and implementations.MethodsWe design a mathematical model to quantify the roles of information superspreaders in single specific information which outbreaks rapidly and usually has a short duration period, and to examine the information propagation dynamics in the Chinese Sina-microblog. Our opinion-leader susceptible-forwarding-immune (OL-SFI) model is formulated to track the temporal evolution of forwarding quantities generated by opinion leaders and normal users.ResultsData fitting from the real data of COVID-19 obtained from Chinese Sina-microblog can identify the different contact rates and forwarding probabilities (and hence calculate the basic information forwarding reproduction number of superspreaders), and can be used to evaluate the roles of opinion leaders in different stages of the information propagation and the outbreak unfolding.ConclusionsThe parameterized model can be used to nearcast the information propagation trend, and the model-based sensitivity analysis can help to explore important factors for the roles of opinion leaders.

COVID-19 information propagation dynamics in the Chinese Sina-microblog.

The outbreak of a novel coronavirus (COVID-19) generated an outbreak of public opinions in the Chinese Sina-microblog. To help in designing effective communication strategies during a major public health emergency, we propose a multiple-information susceptible-discussing-immune (M-SDI) model in order to understand the patterns of key information propagation on social networks. We develop the M-SDI model, based on the public discussion quantity and take into account of the behavior that users may re-enter another related topic or Weibo after discussing one. Data fitting using the real data of COVID-19 public opinion obtained from Chinese Sina-microblog can parameterize the model to make accurate prediction of the public opinion trend until the next major news item occurs. The reproduction ratio has fallen from 1.7769 and maintained around 0.97, which reflects the peak of public opinion has passed but it will continue for a period of time.

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.

Nearcasting forwarding behaviors and information propagation in Chinese Sina-Microblog.

As the largest social media in China, the Sina-Microblog plays an important role in public opinion dissemination. Despite intensive efforts in understanding the information propagation dynamics, the use of a simple outbreak model to generate summative indices that can be used to characterize the time series of a single Weibo event has not been attempted. This work fills this gap, and illustrates the potential of using a simple outbreak model in conjunction with the historical data about the cumulative forwarding users for nearcasting the propagation trend.

AIM: Activation increment minimization strategy for preventing bad information diffusion in OSNs

The openness and virtuality of Online Social Networks (OSNs) make it a hotbed of rapid propagation for various kinds of frauds and erroneous information. Ergo, there is an exigent need to find a method that can expeditiously and efficaciously limit the diffusion of misinformation in OSNs. To resolve this issue, this article proposes the utilization of Activation Increment engendered by a node as a criterion to quantify the importance of the node. Even if the propagation probabilities between the nodes are identically tantamount, due to the dynamics of information propagation and high connectivity of the network, the activation probabilities of nodes are different. The Activation Increment describes the sum of activation probabilities of a node’s neighbors while the node itself is in a different state (infected status, recovered status) at a certain time. To utilize Activation Increment, this paper proposes Activation Increment Minimization (AIM) strategy to select and block nodes for information diffusion. Experiments based on the real social network dataset attested that the proposed AIM strategy is superior to the traditional heuristic algorithms.

Distributed Quickest Detection in Sensor Networks via Two-Layer Large Deviation Analysis

We propose a distributed Bayesian quickest detection algorithm for sensor networks, based on a random gossip inter-sensor communication structure. Without a control or fusion center, each sensor executes its local change detection procedure in a parallel and distributed fashion, interacting with its neighboring sensors via random inter-sensor communications to propagate information. By modeling the information propagation dynamics in the network as a Markov process, a two-layer large deviation analysis is presented to analyze the performance of the proposed algorithm. The first-layer analysis shows that the relation between the probability of false alarm and the conditional averaged detection delay satisfies the large deviation principle, where the distributed Kullback–Leibler information number is established as a crucial factor. The second-layer analysis studies the probability that not all observations are available at one sensor. It shows that this probability decays exponentially fast to zero as the averaged rounds of communication increases. The large deviation upper and lower bounds for the converge rate are then derived. Finally, we show that the performance of the distributed algorithm converges exponentially fast to that of the centralized optimal one.

A topic evolution model with sentiment and selective attention

Topic evolution is a hybrid dynamics of information propagation and opinion interaction. The dynamics of opinion interaction is inherently interwoven with the dynamics of information propagation in the network, owing to the bidirectional influences between interaction and diffusion. The degree of sentiment determines if the topic can continue to spread from this node, and the selective attention determines the information flow direction and communicatee selection. For this end, we put forward a sentiment-based mixed dynamics model with selective attention, and applied the Bayesian updating rules on it. Our model can indirectly describe the isolated users who seem isolated from a topic due to some reasons even everybody around them has heard about it. Numerical simulations show that, more insiders initially and fewer simultaneous spreaders can lessen the extremism. To promote the topic diffusion or restrain the prevailing of extremism, fewer agents with constructive motivation and more agents with no involving motivation are encouraged.

On the Race of Worms and Patches: Modeling the Spread of Information in Wireless Sensor Networks

Sensor networks are a branch of distributed ad hoc networks with a broad range of applications in surveillance and environment monitoring. In these networks, message exchanges are carried out in a multi-hop manner. Due to resource constraints, security professionals often use lightweight protocols, which do not provide adequate security. Even in the absence of constraints, designing a foolproof set of protocols and codes is almost impossible. This leaves the door open to the worms that take advantage of the vulnerabilities to propagate via exploiting the multi-hop message exchange mechanism. This issue has drawn the attention of security researchers recently. In this paper, we investigate the propagation pattern of information in wireless sensor networks based on an extended theory of epidemiology. We develop a geographical susceptible-infective model for this purpose and analytically derive the dynamics of information propagation. Compared with the previous models, ours is more realistic and is distinguished by two key factors that had been neglected before: 1) the proposed model does not purely rely on epidemic theory but rather binds it with geometrical and spatial constraints of real-world sensor networks and 2) it extends to also model the spread dynamics of conflicting information (e.g., a worm and its patch). We do extensive simulations to show the accuracy of our model and compare it with the previous ones. The findings show the common intuition that the infection source is the best location to start patching from, which is not necessarily right. We show that this depends on many factors, including the time it takes for the patch to be developed, worm/patch characteristics as well as the shape of the network.

Information Propagation in Clustered Multilayer Networks

In today's world, individuals interact with each other in more complicated patterns than ever. Some individuals engage through online social networks (e.g., Facebook, Twitter), while some communicate only through conventional ways (e.g., face-to-face). Therefore, understanding the dynamics of information propagation among humans calls for a multi-layer network model where an online social network is conjoined with a physical network. In this work, we initiate a study of information diffusion in a clustered multi-layer network model, where all constituent layers are random networks with high clustering. We assume that information propagates according to the SIR model and with different information transmissibility across the networks. We give results for the conditions, probability, and size of information epidemics, i.e., cases where information starts from a single individual and reaches a positive fraction of the population. We show that increasing the level of clustering in either one of the layers increases the epidemic threshold and decreases the final epidemic size in the whole system. An interesting finding is that information with low transmissibility spreads more effectively with a small but densely connected social network, whereas highly transmissible information spreads better with the help of a large but loosely connected social network.

Impact of individual interest shift on information dissemination in modular networks

Social networks exhibit strong community structure. Many researches have been done to explore the impacts of community structure on information diffusion but few combined with human behaviors together. In this paper, we focus on how the individual interests’ changing behavior impacts the dynamics of information propagation. Firstly, we propose an information dissemination model considering both the community structure and individual interest shift where social reinforcement and time decaying are taken into account. The accuracy of the model is evaluated by comparing the simulation and theoretical results. Further, the numerical results illustrate that both the community structure and the interests changing behavior have effects on the outbreak size of the information dissemination. Specially, lower modularity and higher community connection density will accelerate the speed of information propagation especially when the information maximal lifetime is shorter. In addition, the changes of individual interests in the message have a great impact on the final density of the received through increasing or decreasing the number of satisfied individuals directly. What is more, our findings suggest that when the modularity of the network is higher and the community clustering coefficient is lower individual interest shift behavior will have a heavier effect on the spread scope.

A framework for modeling information propagation of biological systems at critical states

We explore the dynamics of information propagation at the critical state of a biologically inspired system by an individual-based computer model. “Quorum response”, a type of social interaction which has been recognized taxonomically in animal groups, is applied as the sole interaction rule among individuals. In the model, we assume a truncated Gaussian distribution to depict the distribution of the individuals’ vigilance level. Each individual can assume either a naïve state or an alarmed one and only switches from the former state to the latter one. If an individual has turned into an alarmed state, it stays in the state during the process of information propagation. Initially, each individual is set to be at the naïve state and information is tapped into the system by perturbing an individual at the boundaries (alerting it to the alarmed state). The system evolves as individuals turn into the alarmed state, according to the quorum response rules, consecutively. We find that by fine-tuning the parameters of the mean and the standard deviation of the Gaussian distribution, the system is poised at a critical state. We present the phase diagrams to exhibit that the parameter space is divided into a super-critical and a sub-critical zone, in which the dynamics of information propagation varies largely. We then investigate the effects of the individuals’ mobility on the critical state, and allow a proportion of randomly chosen individuals to exchange their positions at each time step. We find that mobility breaks down criticality of the system.

Providing Privacy for User Location by Collaboration

Location-aware smart phones support various location-based services (LBSs): users query the LBS server and learn on the fly about their surroundings. However, such queries give away private information, enabling the LBS to track users. We address this problem by proposing a user-collaborative privacy-preserving approach for LBSs. Our solution does not require changing the LBS server architecture and does not assume third party servers; yet, it significantly improves users’ location privacy. The gain stems from the collaboration of mobile devices: they keep their context information in a buffer and pass it to others seeking such information. Thus, a user remains hidden from the server, unless all the collaborative peers in the vicinity lack the sought information. We evaluate our scheme against the Bayesian localization attacks that allow for strong adversaries who can incorporate prior knowledge in their attacks. We develop a novel epidemic model to capture the, possibly timedependent, dynamics of information propagation among users. Used in the Bayesian inference framework, this model helps analyze the effects of various parameters, such as users’ querying rates and the lifetime of context information, on users’ location privacy. The results show that our scheme hides a high fraction of locationbased queries, thus significantly enhancing users’ location privacy. Our simulations with real mobility traces corroborate our model-based findings. Finally, our implementation on mobile platforms indicates that it is lightweight and the cost of collaboration is negligible. Key words—Mobile Networks, Location-based Services, Location Privacy, Bayesian Inference Attacks, Epidemic Models

PAC Algorithms for Detecting Nash Equilibrium Play in Social Networks: From Twitter to Energy Markets

The detection of agents whose responses satisfy equilibrium play is useful for predicting the dynamics of information propagation in social networks. Using Afriat’s theorem of revealed preferences, we construct a non-parametric detection test to detect if the responses of a group of agents is consistent with play from the Nash equilibrium of a concave potential game. For agents that satisfy the detection test, it is useful to learn the associated concave potential function of the game. In this paper, a non-parametric learning algorithm is provided to estimate the concave potential function of agents with necessary and sufficient conditions on the response class for the algorithm to be a probably approximately correct learning algorithm. In the case of response signals measured in noise, a statistical test to detect agents playing a concave potential game that has a pre-specified Type-I error probability is provided. The detection tests and learning algorithm are applied to real-world data sets from the Twitter social network and the Ontario power grid.