Information Propagation Speed Research Articles

Markovian to non-Markovian phase transition in the operator dynamics of a mobile impurity

We study a random unitary circuit model of an impurity moving through a chaotic medium. The exchange of information between the medium and impurity is controlled by varying the velocity of the impurity, v_dvd, relative to the speed of information propagation within the medium, v_BvB. Above supersonic velocities, v_d> v_Bvd>vB, information cannot flow back to the impurity after it has moved into the medium, and the resulting dynamics are Markovian. Below supersonic velocities, v_d< v_Bvd<vB, the dynamics of the impurity and medium are non-Markovian, and information is able to flow back onto the impurity. We show the two regimes are separated by a continuous phase transition with exponents directly related to the diffusive spreading of operators in the medium. This is demonstrated by monitoring an out-of-time-order correlator (OTOC) in a scenario where the impurity is substituted at an intermediate time. During the Markovian phase, information from the medium cannot transfer onto the replaced impurity, manifesting in no significant operator development. Conversely, in the non-Markovian phase, we observe that operators acquire support on the newly introduced impurity. We also characterize the dynamics using the coherent information and provide two decoders which can efficiently probe the transition between Markovian and non-Markovian information flow. Our work demonstrates that Markovian and non-Markovian dynamics can be separated by a phase transition, and we propose an efficient protocol for observing this transition.

Application of Deep Learning in Social Network Public Opinion Sentiment

With the rapid development of social network platform, in order to purify the network environment, prevent the abuse of public opinion information, and control public opinion in a very short time, this paper proposes the application research of deep learning in social network public opinion sentiment recognition and analysis. Through the construction of social network model and social network knowledge map and the analysis of key technology, a network public opinion algorithm based on deep learning is proposed, and a competitive public opinion information communication model in online social networks is constructed, and then a simulation experiment is conducted on the improved model of the constructed social networks. The result shows that in public opinion management and control, for users with larger node degree, this technology can effectively understand and obtain public opinion information in a very short time with faster and wider information propagation speed, so as to realize the effective control of public opinion information and the processing of massive information.

Identification of influential vertices in complex networks: A hitting time‐based approach

AbstractUnravelling the dynamics of network vertices is pivotal, and traditional centrality measures have limitations in adapting to structural changes, directed and weighted networks, and temporal analyses. This paper introduces a ground breaking approach ‐ hitting time‐based centrality. Utilizing network matrix notations and a random walk model on a connected network , we establish a Markov chain to quantify the hitting time, hitting distance, and hitting centrality, providing a nuanced measure prioritizing central vertices. Through extensive experiments using Kendall's tau coefficient, the paper evaluates the method's correlation with actual influence in the Susceptible‐Infectious (SI) model, showcasing superior performance across diverse network sizes and structures. The hitting centrality method exhibits sensitivity to connectivity dynamics, effective incorporation of temporal dynamics, and robust handling of weighted and directed networks. Positive Kendall's tau coefficients underline the method's proficiency in prioritizing influential vertices by correlating hitting centrality values with actual infection ability. The demonstrated robustness to structural changes enhances its utility for dynamic network analysis. In conclusion, our hitting time‐based centrality approach emerges as a promising method, mitigating the shortcomings of traditional measures. By integrating information propagation speed, accommodating network dynamics, and enabling time‐dependent analyses, it offers a comprehensive tool for evaluating vertex importance and influence in complex networks.

Research on Algorithm-Driven International Chinese Online Language and Culture Dissemination Mechanisms

Abstract With the development of digitalization and Internet technology, algorithm-driven international Chinese online language and culture dissemination mechanisms are becoming increasingly important and a key area of cultural exchange in globalization. This study explores the international Chinese online language and culture communication mechanism under algorithm-driven. By analyzing social network data and adopting quantitative methods such as the SEIR model, the study reveals the effects of network structure, user behavior and cultural content characteristics on communication. The study results show that in the algorithm-optimized network environment, the propagation speed of information is increased by about 50%, and the propagation efficiency of its central node is increased by nearly 70%. Simulations using the SEIR model show that the dissemination coverage of Chinese culture can be increased by more than 40% in a short period with the intervention of the algorithm. In addition, the survey data shows that about 60% of the respondents consider social media the main channel to learn about Chinese culture. The study also found that content with a high degree of interactivity is more likely to be disseminated, with an average interaction rate 30% higher than that of moderate content. Algorithm-driven Chinese cultural communication has higher speed and breadth, but it also needs to pay attention to the depth and diversity of cultural content.

Interplay of a unit-speed constraint and time-delay in the flocking model with internal variables

<p>We studied the dynamics of thermodynamic Cucker–Smale (TCS) particles moving with a constant speed constraint. The TCS model describes the collective dynamics of the population of birds with a time varying internal variable, and it was first introduced as the generalization of the Cucker–Smale (CS) model. In this paper, we considered a modification of the TCS model in which each agent moves at a constant speed, such as the Vicsek model, and we additionally considered the effect of time-delays due to the finiteness of the information propagation speed between agents. Then, we presented several sufficient conditions in terms of initial data and system parameters to exhibit asymptotic flocking. We presented two kinds of results for this purpose. One was an estimate of the diameter of the velocity and temperature configuration, and the other was an estimate of the diameter of the configuration within the time-delay bound $ \tau $.</p>

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.

Hidden influential node selection based on cost–benefit analysis for harmful information propagation control

The quick development of mobile network and smart devices provides a convenience way for information sharing in online social networks, which also accelerates the propagation of harmful information, thus how to select the hidden influential nodes with lower management cost for reducing the propagation speed of harmful information is an important task. In this article, we propose a greedy hidden influential node selection algorithm based on the epidemic model and cost–benefit analysis. First, we investigate the user behaviour dynamic characteristics from two perspectives of social relationships and interaction behaviours, and then susceptible and infected (SI) epidemic model is applied and user influence is estimated. Second, considering the management cost and benefit of different users, a greedy hidden influential node selection algorithm based on the cost–benefit analysis is proposed. Finally, a series of experiments are conducted using the public social network data set and the data set collected from Sina Weibo, to verify the performance and practicality of the developed method. The experimental results demonstrate that our method outperforms other related methods in harmful information propagation control.

Optimal condition for asymptotic consensus in the Hegselmann-Krause model with finite speed of information propagation

We prove that asymptotic global consensus is always reached in the Hegselmann-Krause model with finite speed of information propagation c &gt; 0 {\mathfrak {c}}&gt;0 under minimal (i.e., necessary) assumptions on the influence function. In particular, we assume that the influence function is globally positive, which is necessary for reaching global consensus, and such that the agents move with speeds strictly less than c {\mathfrak {c}} , which is necessary for well-posedness of solutions. From this point of view, our result is optimal. The proof is based on the fact that the state-dependent delay, induced by the finite speed of information propagation, is uniformly bounded.

Behavioral Propagation Based on Passionate Psychology on Single Networks with Limited Contact.

Passionate psychology behavior is a common behavior in everyday society but has been rarely studied on complex networks; so, it needs to be explored in more scenarios. In fact, the limited contact feature network will be closer to the real scene. In this paper, we study the influence of sensitive behavior and the heterogeneity of individual contact ability in a single-layer limited-contact network, and propose a single-layer model with limited contact that includes passionate psychology behaviors. Then, a generalized edge partition theory is used to study the information propagation mechanism of the model. Experimental results show that a cross-phase transition occurs. In this model, when individuals display positive passionate psychology behaviors, the final spreading scope will show a second-order continuous increase. When the individual exhibits negative sensitive behavior, the final spreading scope will show a first-order discontinuous increase In addition, heterogeneity in individuals' limited contact capabilities alters the speed of information propagation and the pattern of global adoption. Eventually, the outcomes of the theoretic analysis match those of the simulations.

Cucker-Smale model with finite speed of information propagation: Well-posedness, flocking and mean-field limit

&lt;p style='text-indent:20px;'&gt;We study a variant of the Cucker-Smale model where information between agents propagates with a finite speed &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ {{\mathfrak{c}}}&amp;gt;0 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. This leads to a system of functional differential equations with state-dependent delay. We prove that, if initially the agents travel slower than &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ {{\mathfrak{c}}} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, then the discrete model admits unique global solutions. Moreover, under a generic assumption on the influence function, we show that there exists a critical information propagation speed &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ {{{{\mathfrak{c}}}^\ast}}&amp;gt;0 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; such that if &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ {{\mathfrak{c}}}\geq{{{{\mathfrak{c}}}^\ast}} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, the system exhibits asymptotic flocking in the sense of the classical definition of Cucker and Smale. For constant initial datum the value of &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ {{{{\mathfrak{c}}}^\ast}} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is explicitly calculable. Finally, we derive a mean-field limit of the discrete system, which is formulated in terms of probability measures on the space of time-dependent trajectories. We show global well-posedness of the mean-field problem and argue that it does not admit a description in terms of the classical Fokker-Planck equation.&lt;/p&gt;

Smart Paradigms of Modern International Mediation

"Over the past few decades, due to increasing interdependence between societies, the speed of information propagation, the diminishing constraints of time and space on communication and the simultaneous involvement of an increment number of actors in the same operating theatre, we have witnessed the globalization of conflict and along with it a remarkable transformation of conflict and how it is fought. There has been a surge of interest in the recent literature on disaggregating war on defining the mechanisms driving non-conventional warfare, and therefore, the aim of this article is to underline the fact that the existing literature on international mediation has placed far too little stress on finding new approaches on managing these new challenges. The international mediation regime did not to match the rhythm and directions of conflict evolutions. Keywords: globalization of conflict, international mediation, smart mediation strategies."

Characterizing the role of influencers in disseminating situational information after an earthquake

Influencers on social media play a crucial role in sharing situational information in disasters. Influencers have been defined in the literature as those with many followers/followees, many likes, actively involved in sharing information, and/or verified by social media platforms. This study collected Yiliang earthquake (Sichuan, China, 2012) data in Weibo and identified five types of situational information using supervised learning and text mining techniques. We found that influencers show (a) significant differences in the timing of sharing situational information; (b) significant positive effects on the propagation scale of all types of situational information, especially for casualties and damages, donations, and criticizing information; and (c) significant positive effects on the propagation speed of casualty and damage information and caution and advice information but significant negative effects on the propagation speed of help-seeking information. These findings have practical implications for authorities and researchers to understand the different roles of influencers in sharing situational information dissemination and designing appropriate influencer-specific information release strategies during a natural disaster.

On the Role of Speed in Technological and Biological Information Transfer for Computations

In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so in real-world implementations, the propagation speed of information cannot exceed the speed of its carrier. Because of this limitation, one must also consider the transfer time between computing units for any implementation. We need a different mathematical method to consider this limitation: classic mathematics can only describe infinitely fast and small computing system implementations. The difference between mathematical handling methods leads to different descriptions of the computing features of the systems. The proposed handling also explains why biological implementations can have lifelong learning and technological ones cannot. Our conclusion about learning matches published experimental evidence, both in biological and technological computing.

Testing the fluctuations of oil resource price volatility: A hurdle for economic recovery

The influence of oil price volatility on significant international macroeconomic indicators is examined empirically. The vector auto-regression (VAR) system is used to examine the influence of oil price volatility. According to the Granger causality test, impulse response functions, and variance decomposition, economic recovery and investment have been significantly affected by oil price volatility from 2000Q1 to 2020Q4. According to this research, business investment and oil prices have shown great power throughout the international economic meltdown. Volatility in economic activity and oil prices are expected during this crisis, according to the recent COVID-19 outbreak. Furthermore, in the international financial crisis and COVID-19 crises, oil prices and economic growth are strongly linked. We propose that the COVID-19 epidemic and the global financial problems have major effects on economic activity when oil prices fall. The COVID-19 epidemic had the greatest total connectedness between oil prices and economic activities, which suggests that the speed of information propagation between the oil market and financial initiatives was greater during the COVID-19 outbreak than during past global financial crises. There are important consequences for policymakers based on the findings of this research.

Emergence of the Fused Spacetime from a Continuum Computing Construct of Reality

Since the emergence of computing as a mode of investigation in the sciences, computational approaches have revolutionised many fields of inquiry. Recently in philosophy, the question has begun rendering bit by bit—could computation be considered a deeper fundamental building block to all of reality? This paper proposes a continuum computing construct, predicated on a set of core computational principles: computability, discretisation, stability and optimisation. The construct is applied to the set of most fundamental physical laws, in the form of non-relativistic conservation equations which underpin our governing physics. The discretisation approach divides all of space into a mesh of discrete computational cells, and evolution of data in those cells must obey pre-defined stability conditions from established continuum computing theory, namely, the Courant–Friedrichs–Lewy condition. Evolving cell-state data in a manner which logically optimises computational efficiency, combined with the defined stability condition, the construct derives a central coupling of space, time, and the fastest speed of information propagation. This coupling formed at the lowest level by the computing construct, naturally and inherently produces aspects of special relativity and general relativity at the macroscale. This paper therefore proposes a new explanation of why the nature of space and time may be fused, and explores simple emergent congruities with relativity. The theory invites us to reverse the philosophical premise of the original Simulation Argument. This argument currently leads us to consider: how might all of the observed physics of our reality be reproduced computationally in a simulation of our existence? This work asks instead: could foundations of the physics of our universe—namely spacetime coupling—emerge inherently, and necessarily, from fundamental principles of computation?

Impact of population topology on particle swarm optimization and its variants: An information propagation perspective

Particle swarm optimization is one of the most effective optimization algorithms motivated by bird flocking behaviours. Population topology is a key aspect of particle swarm optimization research. However, after more than twenty years of research, the effects of the population topology are still poorly understood. Previous research has established that the information propagation speed determined by the population topology has an important impact on the algorithm performance; however, the impact of information propagation speed on particle swarm optimization and its variants has not yet been investigated. In this paper, information propagation in particle swarms is described and, hence, a method of simulating information propagation in particle swarms is introduced, which is used to obtain the information propagation speed. The correlation between the information propagation speed and algorithm performance is clarified through numerical simulation. The results show that the information propagation speed has a strong negative correlation with the population diversity of particle swarm optimization and its variants in the early iterations, regardless of the adopted test function and population diversity measure. The results also show that when optimizing problems with the same property, the impact of population topology on the optimization results of particle swarm optimization and variant algorithms is similar. Further more, this study provides some guidance on the population topology selection for particle swarm optimization and its variants. These findings contribute to our understanding of the impact of population topology on particle swarm optimization and its variants, and provide a basis for population topology selection for particle swarm optimization and its variants.

The speed of information propagation in the scientific network distorts biomedical research.

Delays in the propagation of scientific discoveries across scientific communities have been an oft-maligned feature of scientific research for introducing a bias towards knowledge that is produced within a scientist’s closest community. The vastness of the scientific literature has been commonly blamed for this phenomenon, despite recent improvements in information retrieval and text mining. Its actual negative impact on scientific progress, however, has never been quantified. This analysis attempts to do so by exploring its effects on biomedical discovery, particularly in the discovery of relations between diseases, genes and chemical compounds. Results indicate that the probability that two scientific facts will enable the discovery of a new fact depends on how far apart these two facts were originally within the scientific landscape. In particular, the probability decreases exponentially with the citation distance. Thus, the direction of scientific progress is distorted based on the location in which each scientific fact is published, representing a path-dependent bias in which originally closely-located discoveries drive the sequence of future discoveries. To counter this bias, scientists should open the scope of their scientific work with modern information retrieval and extraction approaches.

SOCIAL MEDIA IN INFORMATION WARFARE. ASSAULT WEAPON WITH HIGH RECOIL

Beyond the benefits or risks of individual or institutional communication through social media, we must note that it is the perfect environment for fake news and propaganda because of the speed of information propagation, the unfriendly environment for checking sources, algorithms behind social networks and, last but not least, the extremely low cost. In other words, the Internet and web 2.0 have created the favorable framework for the conduct of the war "for minds and hearts", as it can be called the information war waged through social media. Beyond these considerations, the non-regulation of the online domain - the lack of rules, be they deontological, make social media a powerful weapon of attack in this type of war. At the same time, the use of this space by state actors should be done with caution because it involves risks that could result in the loss of the most important action capacity: credibility. This article aims to analyze social media as a tool in information warfare

Bit rate bound on superluminal communication

We study semi-classical communication in positivity-violating k-essence scalar field theories, with superluminal modes propagating on a rolling background. The self-interactions due to the non-linear nature of these theories pose a constraint on the rate of superluminal information transfer. We derive a novel bit rate bound on superluminal communication within a conceptual model, to which a general class of k-essence theories naturally reduces. Our result implies the possibility that, even if these positivity-violating k-essence theories may not possess a maximal information propagation speed, there is nevertheless an upper bound on the rate of information transfer.

Well posedness and asymptotic consensus in the Hegselmann-Krause model with finite speed of information propagation

We consider a variant of the Hegselmann-Krause model of consensus formation where information between agents propagates with a finite speed c &gt; 0 \mathfrak {c}&gt;0 . This leads to a system of ordinary differential equations (ODE) with state-dependent delay. Observing that the classical well-posedness theory for ODE systems does not apply, we provide a proof of global existence and uniqueness of solutions of the model. We prove that asymptotic consensus is always reached in the spatially one-dimensional setting of the model, as long as agents travel slower than c \mathfrak {c} . We also provide sufficient conditions for asymptotic consensus in the spatially multidimensional setting.