Information Dissemination In Networks Research Articles

PROTECTION OF HUMAN HONOUR AND DIGNITY IN SOCIAL NETWORKS: PROBLEMS AND POSSIBLE SOLUTIONS

This article is devoted to the analysis of the problems of protecting human honour and dignity in social networks. The article reveals three problems on the subject of the study – the widespread use of social networks, virtually unlimited freedom of information dissemination in social networks, as well as the latency of offenses committed in the virtual space. Based on the analysis of the reasons and opinions of some scientists, the author of the article proposes to implement measures in four directions – i) the formation of reasonable legal regulation, ii) strengthening international cooperation, iii) taking organizational and technical measures, iv) increasing the level of legal culture and ethics in social networks. Based on the results of the article, the author concludes that the legal regulation of social networks for the protection of human honour and dignity should be as specific and sufficient as possible.

Representation Learning Method Based on Improved Random Walk for Influence Maximization

The purpose of the influence maximization problem is to determine a subset to maximize the number of affected users. This problem is very crucial for information dissemination in social networks. Most traditional influence maximization methods usually focus too heavily on the information diffusion model and randomly set influence parameters, resulting in inaccurate final outcomes. Driven by the recent criticisms of the diffusion model and the rapid development of representation learning, this paper proposes a representation learning method based on improved random walk for influence maximization (IRWIM) to maximize the influence spread. The IRWIM algorithm improves the traditional random walk and adopts multi-task neural network architecture to predict the propagation ability of nodes more accurately. Moreover, the greedy strategy is utilized to continuously optimize the marginal gain while retaining the theoretical guarantee. IRWIM is tested on four genuine datasets. Experimental results show that the accuracy of the proposed algorithm is superior to various competitive algorithms in the field of influence maximization.

Measuring Academic Representative Papers Based on Graph Autoencoder Framework

Objectively evaluating representative papers in a specific scientific research field is of great significance to the development of academia and scientific research institutions. Representative papers on achievements in scientific research can reflect the academic level and research characteristics of researchers and research institutions. The existing research methods are mainly based on external feature indicators and citation analysis methods, and the method of combining artificial intelligence is in its infancy. From the perspective of scientific research institutions, this paper proposes a graph autoencoder framework based on heterogeneous networks for the measurement of paper impact, named GAEPIM. Specifically, we propose two versions of GAEPIM based on a graph convolutional network and graph transformer network. The models rank papers in a specific research field and find the most representative papers and their scientific institutions. The proposed framework constructs a heterogeneous network of papers, institutions, and venues and simultaneously analyzes the semantic information of papers and the heterogeneous network structural information. Finally, based on the complex network information diffusion model, the proposed method performs better than several widely used baseline methods.

Minimal Doubly Resolving Sets of Certain Families of Toeplitz Graph

The doubly resolving sets are a natural tool to identify where diffusion occurs in a complicated network. Many real-world phenomena, such as rumour spreading on social networks, the spread of infectious diseases, and the spread of the virus on the internet, may be modelled using information diffusion in networks. It is obviously impractical to monitor every node due to cost and overhead limits because there are too many nodes in the network, some of which may be unable or unwilling to send information about their state. As a result, the source localization problem is to find the number of nodes in the network that best explains the observed diffusion. This problem can be successfully solved by using its relationship with the well-studied related minimal doubly resolving set problem, which minimizes the number of observers required for accurate detection. This paper aims to investigate the minimal doubly resolving set for certain families of Toeplitz graph Tn(1, t), for t ≥ 2 and n ≥ t + 2. We come to the conclusion that for Tn(1, 2), the metric and double metric dimensions are equal and for Tn(1, 4), the double metric dimension is exactly one more than the metric dimension. Also, the double metric dimension for Tn(1, 3) is equal to the metric dimension for n = 5, 6, 7 and one greater than the metric dimension for n ≥ 8.

The effect of opinion emotion on information dissemination in social networks

When popular topics arise, Internet users typically share their opinions and interact with others on social networking platforms, and the comments generated on social networks contribute to the spread of popular trends. In this paper, we focus on the popular ChatGPT topic and analyze the evolutionary trend of public opinion and the emotional polarity of comments related to ChatGPT on the Weibo and WeChat social media platforms. In this paper, emotions are divided into six categories, the emotions contained in Weibo and WeChat comments are discussed, and the main opinions reflected in each emotion are examined. We expect that our finding will help reveal the relationship between opinion and sentiment.

Asymmetric Information Dissemination in Double-Layer Networks Helps Explain the Emergence of Cooperation

This paper proposes a double-layer network game model based on asymmetric information, hoping to explore the impact of asymmetric information dissemination on the evolution of cooperation. The model assumes that agents in heterogeneous states play the Prisoner’s Dilemma game in the network’s physical layer, and heterogeneous information disseminates asymmetrically in the virtual layer of the network. Through mean-field theory analysis and Monte Carlo experiments, we found that the dissemination of asymmetric information significantly impacts cooperation. The positive information generated by the defector can promote cooperation in the short term but will hinder cooperation in the long term. Positive information generated by cooperators can promote cooperation in the long run. The system’s final state depends on the relative intensity of the two kinds of information dissemination. Asymmetric information dissemination can promote cooperation because heterogeneous information has distinct dissemination intensities, which makes the number of active agents around the agents different. The positive information generated by cooperators can attract more active agents in the long run, thus obtaining more payoffs, making the agents in the system tend to cooperate. The positive information generated by defectors produces more silent neighbours in the long run, thus reducing the overall payoffs, which makes the agents in the system tend to defect. This paper provides a new explanation for the emergence of cooperation, which helps expand the existing research field.

Dynamic Simulation Study on Evolution Law and Intervention Strategy of Public Opinion Information of Major Public Health Events

Compared with the traditional media era, the network information production and dissemination mode in the new media era has undergone essential changes. Especially after the outbreak of major public health events of public concern, a variety of public opinion information spread together, and the evolution law of network public opinion becomes more complex, which easily leads to the outbreak of a public opinion crisis. Therefore, it is of great significance to master the evolution law of public opinion information about major public health events and scientifically intervene and guide public opinion for event handling and maintaining social stability, and it is also a crucial part of the field of public management at present. Based on SEIR infectious disease model, this paper analyzed the dynamic characteristics of dissemination of network public opinion information and built a simulation model using the dynamic simulation method on the NetLogo simulation platform to simulate the evolution law of network public opinion of major public health events, analyzed the expected effect of public opinion intervention and public opinion guidance strategies, and put forward a more scientific idea of public opinion intervention and public opinion guidance.

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments.

Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca. Here, we used an EEG dataset to investigate the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures calculated from (B). Further, at the abstraction level of (C), we developed new measures of complex networks relating to community detection. As a result, the machine learning method was able to automatically detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are more important for the detection of ayahuasca. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. F3 and PO4 were the most important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity were the most important complex network measures. These two measures are also associated with diseases such as Alzheimer's disease, indicating a possible therapeutic mechanism. Moreover, the new measures were crucial to the predictive model and suggested larger brain communities associated with the use of ayahuasca. This suggests that the dissemination of information in functional brain networks is slower when this drug is present. Overall, our methodology was able to automatically detect changes in brain activity during ayahuasca consumption and interpret how these psychedelics alter brain networks, as well as provide insights into their mechanisms of action.

Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic.

Social media is not only an essential platform for the dissemination of public health-related information, but also an important channel for people to communicate during the COVID-19 pandemic. However, social bots can interfere with the social media topics that humans follow. We analyzed and visualized Twitter data during the prevalence of the Wuhan lab leak theory and discovered that 29% of the accounts participating in the discussion were social bots. We found evidence that social bots play an essential mediating role in communication networks. Although human accounts have a more direct influence on the information diffusion network, social bots have a more indirect influence. Unverified social bot accounts retweet more, and through multiple levels of diffusion, humans are vulnerable to messages manipulated by bots, driving the spread of unverified messages across social media. These findings show that limiting the use of social bots might be an effective method to minimize the spread of conspiracy theories and hate speech online.

The global neuronal workspace as a broadcasting network.

A new strategy for moving forward in the characterization of the global neuronal workspace (GNW) is proposed. According to Dehaene, Changeux, and colleagues (Dehaene, 2014, pp. 304, 312; Dehaene & Changeux, 2004, 2005), broadcasting is the main function of the GNW. However, the dynamic network properties described by recent graph theoretic GNW models are consistent with many large-scale communication processes that are different from broadcasting. We propose to apply a different graph theoretic approach, originally developed for optimizing information dissemination in communication networks, which can be used to identify the pattern of frequency and phase-specific directed functional connections that the GNW would exhibit only if it were a broadcasting network.

Analysis of information propagation and control of a layered SITR model in complex networks

In the field of complex network research, complex network information transmission models based on infectious disease models are often used to study the mechanism of information transmission. This is helpful for the prediction of information transmission trends and the formulation of control strategies. However, the classification of node types in traditional information transmission models is too simple and cannot reflect the characteristics of each node. To solve the above problems, this study proposes a layered SITR complex network information transmission model. The model is layered according to the influence of nodes, and rational propagator nodes are added to optimize it. The propagation threshold of the model is deduced theoretically and the stability of the model is proved. To reduce the dissemination scale of the network’s public opinion information, an optimal control strategy is proposed based on the Pontryagin maximum principle to optimize the information dissemination process. Finally, combined with real events from social network platform, the simulation results show that the layered SITR model can describe the process of network information dissemination more accurately, and the optimal control strategy can effectively reduce the dissemination scale of the network’s public opinion information.

Distance and health facility choice: Evidence from a health micro insurance program in Punjab, Pakistan.

Health micro insurance offers a promising mechanism to protect the poor against risk and vulnerability arising from catastrophic healthcare expenditures. In light of this, we study the relationship between physical distance to hospitals and the choice of healthcare services in the context of a health micro insurance program in Punjab, Pakistan. We address three main research questions; first, how does physical distance affect choice of health facility? Second, is the burden of physical distance greater for women? Third, can the diffusion of information in social networks be a potential mechanism for reducing the burden of distance? We employ a Probit model with administrative data on hospitalisation claims made between 2014 and 2017. Our findings show that distance impedes individuals from making panel (cashless) claims and thus increases the likelihood of out-of-pocket expenditures at nearby non-panel hospitals. This adverse effect is more pronounced for women as compared to men. Dissemination of information in social networks increases the usage of panel facilities, especially by women. Hence, this can be an effective mechanism in reducing the role that distance plays in the choice of health facility.

Research on older people's health information search behavior based on risk perception in social networks-A case study in China during COVID-19.

ObjectiveCOVID-19 has caused great loss of human life and livelihoods. The dissemination of health information in online social networks increased during the pandemic's quarantine. Older people are the most vulnerable group in sudden public health emergencies, and they have the disadvantage of infection rates and online search for health information. This study explores the relationship between the health risk perception and health information search behavior of older people in social networks, to help them make better use of the positive role of social networks in public health emergencies.MethodBased on the Risk Information Search and Processing model, and in the specific context of COVID-19, this study redefines health risk perception as a second-order construct of four first-order factors (perceived probability, perceived severity, perceived controllability, and perceived familiarity), and constructs a research model of the health risk perception and health information search behavior of older people. An online survey of people over 55 years old was conducted through convenience sampling in China from February 2020 to March 2020.ResultsA total of 646 older adults completed the survey. The structural equation model showed that health risk perception is a second-order factor (H1), that health risk perception has significant positive effects on health information search behavior (H2: β = 0.470, T = 11.577, P < 0.001), and that health risk perception has significant positive effects on affective response (H3: β = 0.536, T = 17.356, P < 0.001). In addition, affective response has a significant positive mediating effect on information sufficiency (H4: β = 0.435, T = 12.231, P < 0.001), and information sufficiency has a significant positive mediating effect on health information search behavior (H5: β = 0.136, T = 3.081, P = 0.002).ConclusionThe study results indicate that the health risk perception of older people during the COVID-19 outbreak not only directly affected their health information search behavior, but also had an indirect impact on their health information search behavior by affecting affective response and information sufficiency.

Effect of Blended Teaching Mode in Colleges and Universities Based on Automation Technology on College Students’ English Performance

In today’s society, the rapid development of information technology has had a profound impact on human work and life as well as on education. In recent years, the rapid development of the Internet and information technology has laid a solid foundation for the digital transmission of information, which has changed the mode of information transmission and improved the efficiency of information dissemination. As an important part of network information dissemination, blended teaching is rapidly developing from an on-demand learning platform to an interactive learning platform and has now become a blended teaching platform in China. The main problem of the current blended teaching model is that the effect of improving performance is not good. In order to solve this problem, this paper introduces automation technology, which can significantly improve the extraction accuracy of information extraction methods in specific fields by using rich domain knowledge and features and complete the extraction tasks at multiple levels. The automation method based on automation technology has great advantages in this regard and effectively improves the English performance. Two groups of voluntary college students were randomly selected to conduct a one-semester blended teaching model designed in this paper to investigate the ’students’ English scores. The average grades of students were 95.2, 96.7, 96.8, and 97.2, respectively, which is a big improvement over the existing system.

Dynamic graph neural network for fake news detection

The widespread of fake news on social media and other platforms can bring significant damage to the harmony and stability of our society. To defend against fake news, researchers have suggested various ways of dealing with fake news. In recent years, fake news detection has become the research focus in both academic and industrial communities. The majority of existing propagation-based fake news detection algorithms are based on static networks and they assume the whole information propagation network structure is readily available before performing fake news detection algorithms. However, real-world information diffusion networks are dynamic as new nodes joining the network and new edges being created. To address these shortcomings, we proposed a dynamic propagation graph-based fake news detection method to capture the missing dynamic propagation information in static networks and classify fake news. Specifically, the proposed method models each news propagation graph as a series of graph snapshots recorded at discrete time steps. We evaluate our approach on three real-world benchmark datasets, and the experimental results demonstrate the effectiveness of the proposed model.

Exploring a Multi-Layer Coupled Network Propagation Model Based on Information Diffusion and Bounded Trust

Objective: To explore the law of opinion dissemination and individual opinion evolution at the micro level, this paper analyzes the influence of variation and oyster on communication from the perspective of network structure. Methods: In this paper, we introduce the concepts of “variation” and “oyster”, build a multi-layer coupled network environment combined with the ISOVR model, and conduct simulation experiments of network information dissemination based on the bounded trust model. Results: The experimental results reveal that the extent and scope of variation’s spread in the network are more dependent on the trust of nodes themselves, and decreasing the trust of nodes significantly reduces the rate and peak value of variation. Changing the silence coefficient of variation does not effectively change the direction of rumor propagation, which indicates that rumor has a strong propagation ability after mutation. Conclusion: The insights of this paper on the dissemination of public opinions include: 1) pay attention to people with high trust levels, such as opinion leaders; 2) clarify the misinformation in time to prevent further spread of rumors.

Hedge fund networks, information dissemination, and stock price comovement: Evidence from China

We define a hedge fund network as consisting of weak ties based on common holdings. By creating a filtered dataset of stocks held by hedge funds in China from 2010 to 2019, we examine the effect of the network on stock return comovement and further test the incentive-compatible conditions for honest communication between competitors (Stein, 2008). First, we find that the weak ties in a social network affect the behaviour of hedge fund managers, when formal institutions are imperfect. Second, we find that listed firms held by hedge funds are exposed to an information network that is composed of hedge funds. The higher the hedge fund network density is, the more likely information dissemination is, and the weaker the stock price comovement is. Third, our results support Stein's (2008) two major conclusions: a network effect exists only within stable relationships, and the more central a stock is in the network, the stronger the network effect is. After controlling for various external factors that might influence the hedge fund network's information diffusion mechanism, all the results meet our theoretical expectations. Overall, we contribute to the literature by determining the role of the hedge fund network as an information transmitter in weak ties and by providing empirical evidence on the theory of honest communication among competitors.

Influence of Repetition through Limited Recall

Decision makers who receive many signals are subject to imperfect recall. This is especially important when learning from feeds that aggregate messages from many senders on social media platforms. In this paper, we study a stylized model of learning from feeds and highlight the inefficiencies that arise due to imperfect recall. In our model, failure to recall a specific message comes from the accumulation of messages which creates interference. We characterize the influence of each sender according to the rate at which she sends messages and to the strength of interference. Our analysis indicates that imperfect recall not only leads to double-counting and extreme opinions in finite populations, but also impedes the ability of the receiver to learn the true state as the population of the senders increases. We estimate the strength of interference in an online experiment where participants are exposed to (non-informative) repeated messages and they need to estimate the opinion of others. Results show that interference plays a significant role and is weaker among participants who disagree with each other. Our work has implication for the diffusion of information in networks, especially when it is false because it is shared and repeated more than true information.

News Hotspot Event Diffusion Mechanism Based on Complex Network

The wide range of social hot news events on the Internet has made the Internet have a great impact on the public. However, there are few studies on Internet information. In order to improve the efficiency of user network information dissemination of Internet information based on complex network theory and model simulation, this paper makes a more in-depth study on information dissemination on the Internet, constructs a complex network of Internet information dissemination, and analyzes the static topology and dynamic evolution process of the network. Using the attention relationship between Internet users, the Internet information dissemination network, degree, and path were used to select multiple indicators. The static topology of the network is analyzed by using the complex network theory. The study found that the complex network of Internet information is different from other complex networks. The influencing factors of network dynamic evolution are studied from three aspects: overall structure, local structure, and time constraints. The evolution trend of different forms and overall network nodes in the evolution process was explored, and the network dynamic evolution process model was constructed. Practice shows that the model can better describe the evolution process of network information dissemination in complex networks. The degree values of the two networks are positively correlated with the corresponding average clustering coefficients, and the networks have a significant hierarchy. The correlation between news hot events and network nodes is not as good as users’ attention to the network.

Age of Information Modeling and Optimization for Fast Information Dissemination in Vehicular Social Networks

Autonomous vehicles (AVs) with advanced communication, computing, and control capabilities will provide not only a convenient means of transportation but also an emerging platform for real-time social communications and networking. Thereby, it is crucial to enable timely exchange of information over the dynamic cyber-physical-social system enabled by the AVs. In this paper, we consider age of information (AoI)-centric real-time information dissemination over vehicular social networks (VSNs), where the AoI captures the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">freshness</i> of received data packets. We first propose a mathematical framework based on the mean-field theory (MFT) to analyze the network AoI (NAoI) of VSNs, namely AoI for the AV that lastly receives the information update in the network. The proposed analytical framework considers both the social features of vehicular networks, which are characterized using the scale-free network theory, and the underlying wireless communication process to evaluate the NAoI. Then, we further consider joint optimization of the information update rate at the source node and the transmit probabilities at the AVs for minimization of the average peak NAoI (PNAoI), i.e., the time average of peak values occurred in the evolution of the NAoI, which is solved via a novel parametric optimization scheme. Both analytical and simulation results show that compared with several baseline schemes, the proposed scheme can exploit the scale-free feature of the VSNs to significantly lower the average PNAoI by up to 96 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .