Information Spreading Process Research Articles

The fluctuation analysis of public opinion energy: Modeling social group opinion base on the event of social networks

In the period of Corona Virus Disease 2019 (COVID-19), millions of people participate in the discussion of COVID-19 on the Internet, which can easily trigger public opinion and threaten social stability. To find out the relationship between the intergroup variability in numbers and perspectives and the dynamic change of the number of infected people, this paper defines the public focus level to quantify the level of attention of people to the information related to an epidemic situation, and the POF model based on the level of epidemic focus is proposed. In this paper, we have carried out simulation experiments in small-world networks and scale-free networks, respectively, to explore the relationship between the model parameters and the spreading range and speed of each population. Furthermore, the paper also analyzed all the original microblog posts published by the People's Daily from January 14, 2020, to February 12, 2020, and compared the data simulated by the POF model with the real data from the People's Daily, the simulation data and the real data can be well fitted to prove the reliability of the model.

A novel potential edge weight method for identifying influential nodes in complex networks based on neighborhood and position

The identification of influential nodes is one of the most urgent and challenging research issues in complex networks, which is crucial to the robustness and stability of networks. Edges in networks are vital communication channels in the information spreading process, many existing methods for identifying influential nodes are established via employing characteristics of nodes, but most of them consider the edges equally in unweighted networks. In this paper, we introduce a novel potential edge weight to distinguish the influence of each edge by utilizing the H-index, k-shell iteration factor and clustering coefficient of its connection nodes. A novel potential edge weight method, called the HIC centrality (HIC), is proposed to identify influential nodes in complex networks. The key idea of HIC is to incorporate neighborhood, position, and topological structure features to identify influential nodes. To evaluate the performance of HIC, we adopt the susceptible–infected–recovered (SIR) model and the susceptible–infected (SI) model for simulation and conduct a series of comparative experiments on nine real-world networks and three artificial networks. Experimental results show that the proposed method is superior to seven contrast methods in terms of accuracy, effectiveness and distinguishing ability, including five centrality measures and two popular potential edge weight methods. The HIC centrality has low computational complexity, allowing it to be applied to sparse large-scale networks.

Emergency of Tsallis statistics in fractal networks.

Scale-free networks constitute a fast-developing field that has already provided us with important tools to understand natural and social phenomena. From biological systems to environmental modifications, from quantum fields to high energy collisions, or from the number of contacts one person has, on average, to the flux of vehicles in the streets of urban centres, all these complex, non-linear problems are better understood under the light of the scale-free network's properties. A few mechanisms have been found to explain the emergence of scale invariance in complex networks, and here we discuss a mechanism based on the way information is locally spread among agents in a scale-free network. We show that the correct description of the information dynamics is given in terms of the q-exponential function, with the power-law behaviour arising in the asymptotic limit. This result shows that the best statistical approach to the information dynamics is given by Tsallis Statistics. We discuss the main properties of the information spreading process in the network and analyse the role and behaviour of some of the parameters as the number of agents increases. The different mechanisms for optimization of the information spread are discussed.

The influence of heterogeneity of adoption thresholds on limited information spreading

The spreading process of information on complex networks has been widely explored. In fact, different individuals in a network usually hold different standards for information adoption. Considering the heterogeneity of adoption thresholds, this study constructs a two-layer network model with limited contacts. The adoption threshold of a node is related to its degree and a parameter obeying truncated normal distribution. This study also proposes a partition theory based on edges to analyze the mechanism of information dissemination quantitatively. Experiments find that increasing the mean of parameters can inhibit information from spreading, and the effect of the standard deviation of parameters on information dissemination depends on the mean of parameters. For instance, when the mean of parameters is a low value, as the standard deviation of parameters increases, the information outbreak size will decrease. On the other hand, the information outbreak size will increase continuously with increased propagation probability. If the mean of parameters is high, the information outbreak size will increase first and then decrease with the increment in the standard deviation of parameters. The theoretical predictions of this study are in good agreement with the numerical simulations.

Identifying Influential Nodes in Complex Networks Based on Neighborhood Entropy Centrality

AbstractIdentifying influential nodes is a fundamental and open issue in analysis of the complex networks. The measurement of the spreading capabilities of nodes is an attractive challenge in this field. Node centrality is one of the most popular methods used to identify the influential nodes, which includes the degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC). The DC is an efficient method but not effective. The BC and CC are effective but not efficient. They have high computational complexity. To balance the effectiveness and efficiency, this paper proposes the neighborhood entropy centrality to rank the influential nodes. The proposed method uses the notion of entropy to improve the DC. For evaluating the performance, the susceptible-infected-recovered model is used to simulate the information spreading process of messages on nine real-world networks. The experimental results reveal the accuracy and efficiency of the proposed method.

Faster heuristics for graph burning.

Graph burning is a process of information spreading through the network by an agent in discrete steps. The problem is to find an optimal sequence of nodes that have to be given information so that the network is covered in least number of steps. Graph burning problem is NP-Hard for which two approximation algorithms and a few heuristics have been proposed in the literature. In this work, we propose three heuristics, namely, Backbone Based Greedy Heuristic (BBGH), Improved Cutting Corners Heuristic (ICCH), and Component Based Recursive Heuristic (CBRH). These are mainly based on Eigenvector centrality measure. BBGH finds a backbone of the network and picks vertex to be burned greedily from the vertices of the backbone. ICCH is a shortest path based heuristic and picks vertex to burn greedily from best central nodes. The burning number problem on disconnected graphs is harder than on the connected graphs. For example, burning number problem is easy on a path where as it is NP-Hard on disjoint paths. In practice, large networks are generally disconnected and moreover even if the input graph is connected, during the burning process the graph among the unburned vertices may be disconnected. For disconnected graphs, ordering the components is crucial. Our CBRH works well on disconnected graphs as it prioritizes the components. All the heuristics have been implemented and tested on several bench-mark networks including large networks of size more than 50K nodes. The experimentation also includes comparison to the approximation algorithms. The advantages of our algorithms are that they are much simpler to implement and also several orders faster than the heuristics proposed in the literature.

Dynamic Mathematical Model of Information Spreading on News Platform

In recent years, the news platform has become the primary source of information for users. However, there are few studies on the news platform, especially for the analysis and modeling of the spreading process of information. This article models the dynamic process of information spreading on the news platform. Firstly, we analyze the dynamic characteristics of user state and information value. Users of news platforms have two states, active and silent states, and users can switch between these two states. The information value determines the probability of user state conversion. We construct the mathematical model for the dynamic features of user state and information value considering these characteristics. Then, with appropriate parameter assumptions, simulation experiments are performed to analyze the regularity of information spreading. The results of the experiment show that the user’s reading speed 1/r and the conversion probability p(α, β, t) are important indicators that affect user state conversion. The lower reading speed and higher conversion probability can improve the transformation of the user state. Furthermore, we present some applications to promote information spreading, such as assessing the effectiveness of information spreading and controlling rumors on news platforms. Finally, we analyzed the effect of its information dissemination by taking Toutiao as an example and confirmed that the visibility and quality of information are important factors that affect information spreading. The experiments and analysis show that the dynamic mathematical model can reflect the information spreading in different situations with different parameters on the news platform.

The Spread of Information in Virtual Communities

With the growth of online commerce, companies have created virtual communities (VCs) where users can create posts and reply to posts about the company’s products. VCs can be represented as networks, with users as nodes and relationships between users as edges. Information propagates through edges. In VC studies, it is important to know how the number of topics concerning the product grows over time and what network features make a user more influential than others in the information-spreading process. The existing literature has not provided a quantitative method with which to determine key points during the topic emergence process. Also, few researchers have considered the link between multilayer physical features and the nodes’ spreading influence. In this paper, we present two new ideas to enrich network theory as applied to VCs: a novel application of an adjusted coefficient of determination to topic growth and an adjustment to the Jaccard coefficient to measure the connection between two users. A two-layer network model was first used to study the spread of topics through a VC. A random forest method was then applied to rank various factors that might determine an individual user’s importance in topic spreading through a VC. Our research provides insightful ways for enterprises to mine information from VCs.

Identification of Influential Spreaders in Social Networks using Improved Hybrid Rank Method

Abstract Online Social Networks provide us a gateway towards valuable information regarding personal preferences, interests, and connections. Ranking nodes by centrality measures help us to identify some of the most critical and influential nodes in a network who play a crucial role in the information spreading process and influence maximization. Traditional centrality measures have certain restraints and do not provide optimal results single-handedly. With the advances in research, leading to the formulation of the concept of hybrid centrality, the detection of the most influential spreaders in a network has shown a large scale of improvement. Some of the real-world phenomena such as viral marketing, rumor spreading, etc. can be addressed by leveraging the fact that only the dominant and authoritative nodes play a crucial role in propagating information about a new product or idea and those nodes can also help in blocking false information or rumors from reaching the other parts of the network. In this paper, we present the Improved Hybrid Rank algorithm, which combines two centralities, namely, the Extended Neighborhood Coreness centrality and the H-Index centrality. The results obtained by simulating our proposed method using the SIR (Susceptible-Infected-Recovered) model on different un-directed and directed real-world networks show that the ranking of nodes and the choice of influential spreaders, as proposed by our algorithm outperforms many other classical methods.

Information Spreading on Two-Layered Multiplex Networks With Limited Contact

Information spreading on multiplex complex networks has been widely concerned. In reality, the constraints of inelastic resources, such as energy, time, money, and an individual's restrict contact capacities, can affect the information spreading process. In this paper, we propose an information spreading model with limited contact capacity on the two-layered multiplex network. In order to quantitatively study the information spreading, we adopt an edge-based compartmental theory. We find that increasing the contact capacity facilitates information spreading. If the multiplex networks with strong heterogeneous degree distributions, the information outbreak size grows continuously with the transmission probability. However, when the information spreads on multiplex networks with homogeneous or heterogeneous degree distributions, there exists a crossover phenomenon, in which the information outbreak size versus the transmission probability can grow continuously or discontinuously. Our theory agrees well with the numerical simulations.

Modeling the Performance of Limiting Information Spreading Processes under Competing Linear Threshold Model

Abstract Social networks create routes for information transmission with the use of social influence mechanisms. While one of goals can be to increase the dynamics of processes within the networks, from the perspective of the spread of harmful content or misleading information, efforts should be focused on limiting the spread. One possible strategy is the initialisation of competing processes as a reaction to negative spread. Depending on the parameters of the limiting process and the network topologies, actions can succeed or fail. In this paper, we focused on a linear threshold competing model and analysed the effects of the limiting processes within scale-free networks with different characteristics, as well as the process parameters and timing. The results showed how the parameters of the limiting process should be adjusted to achieve substantial coverage within the network depending on the network characteristics and the intervals between the positive and negative processes.

Effects of two channels on explosive information spreading

Information spreading has been studied for decades, but the underlying mechanism why the information can be accepted by a large number of people overnight is still under debate, especially in the aspects of two-channel effects for information transmission and theoretical analysis. In this study, based on a susceptible-accepted-recovered (SAR) model, we examine the effects of two channels represented by a two-layered network, in which one channel is the intra-links within the same layer and the other is inter-links between layers. Different with the case of one single channel on a one-layered network, in the case of two channels, the spreading can be speeded up by the increase in the coupling strength, i.e., average node degree and transmission probability between the two layers. Strikingly, even if the parameter (social reinforcement) is small, the strongly coupling strength can induce explosive transition in the information spreading process. Additionally, a big gap closed to the critical point for the explosive transition was found in the phase space of theoretical analysis, which indicates the emergency of a global large-scope outbreak. These findings may be of significance on the understanding and controlling explosive information spreading in modern society.

Analysis the solutions of the differential non-linear equations describing the information spreading process with jump discontinuity

Analysis the solutions of the differential non-linear equations describing the information spreading process with jump discontinuity

Community detection based on human social behavior

Community structure is widespread in the form of complex networks and is important. However, research on detecting community structure (community detection) still has some unsolved problems. In this paper, we introduce a new community detection algorithm, called Commansor (Community Detection Based on HumanSocial Behavior), which automatically detects communities in the network by simulating human social behavior. The fundamental principle of Commansor is finding the social comfort zone of human beings in the social process and simulating the evolution of social network structure in the information spreading process. Different closeness relationships between nodes form a comfort zone for each node, and the process of information spreading has an important impact on the evolution of the community structure. The main steps of the Commansor algorithm include (a) finding a comfort zone for each node by using closeness matrices, (b) randomly selecting an associated node from the comfort zone for each node to reconstruct a simplified network, and (c) adjusting the simplified network structure by simulating the process of information spreading in human society. We compared Commansor with a range of typical algorithms by performing extensive experiments on synthetic networks and real-world networks. The experiments proved that, in most cases, our algorithm is superior to the compared algorithms in terms of the quality of community detection.

Identifying influential spreaders in large-scale networks based on evidence theory

Identifying the most influential spreaders is an important issue in epidemic spreading, viral marketing, and controlling the spreading process of information. Thus, methods for identifying influential spreaders in complex networks have received increasing attention from researchers. During recent decades, researchers have proposed many methods. However, each of these methods has advantages and disadvantages. In this paper, we propose a new efficient algorithm for identifying influential spreaders based on the Dempster–Shafer (D–S) evidence theory, which is a complete theory that deal with uncertainty or imprecision. We call our proposed algorithm D-2SN, which trades off between the degree (D) and the 2-step neighbor information (2SN) of every node in a network. Specifically, the influence of both the degree and the 2SN of each node are represented by a basic probability assignment (BPA). D-2SN is determined by the fusion of these BPAs. Since the algorithm considers not only the topological structure of each node, but also its neighbors’ structure, it is a good choice to balance cost and performance. In addition, it also exhibits very low time complexity O(⟨k⟩n), which makes it applicable to large-scale networks. To evaluate the performance of D-2SN, we employ the Independent Cascade (IC) and Liner Threshold (LT) models to examine the spreading efficiency of each node and compare D-2SN with several classic methods in eight real-world networks. Extensive experiments demonstrate the superiority of D-2SN to other baseline methods.

Modelling the Impact of Transit Media on Information Spreading in an Urban Space Using Cellular Automata

Information spreading processes are the key drivers of marketing campaigns. Activity on social media delivers more detailed information compared to viral marketing in traditional media. Monitoring the performance of outdoor campaigns that are carried out using this transportation system is even more complicated because of the lack of data. The approach that is presented in this paper is based on cellular automata and enables the modelling of the information-spreading processes that are initiated by transit advertising within an urban space. The evaluation of classical and graph cellular automata models and a coverage analysis of transit advertising based on tram lines were performed. The results demonstrated how the number of lines affects the performance in terms of coverage within an urban space and the differences between the proposed models. While research is based on an exemplary dataset taken from Szczecin (Poland), the presented framework can be used together with data from the public transport system for modelling advertising resources usage and coverage within the urban space.

Self-avoiding pruning random walk on signed network

A signed network represents how a set of nodes are connected by two logically contradictory types of links: positive and negative links. In a signed products network, two products can be complementary (purchased together) or substitutable (purchased instead of each other). Such contradictory types of links may play dramatically different roles in the spreading process of information, opinion, behaviour etc. In this work, we propose a self-avoiding pruning (SAP) random walk on a signed network to model e.g. a user’s purchase activity on a signed products network. A SAP walk starts at a random node. At each step, the walker moves to a positive neighbour that is randomly selected, the previously visited node is removed and each of its negative neighbours are removed independently with a pruning probability r. We explored both analytically and numerically how signed network topological features influence the key performance of a SAP walk: the evolution of the pruned network resulted from the node removals, the length of a SAP walk and the visiting probability of each node. These findings in signed network models are further verified in two real-world signed networks. Our findings may inspire the design of recommender systems regarding how recommendations and competitions may influence consumers’ purchases and products’ popularity.

Optimal modularity and memory capacity of neural reservoirs.

The neural network is a powerful computing framework that has been exploited by biological evolution and by humans for solving diverse problems. Although the computational capabilities of neural networks are determined by their structure, the current understanding of the relationships between a neural network’s architecture and function is still primitive. Here we reveal that a neural network’s modular architecture plays a vital role in determining the neural dynamics and memory performance of the network of threshold neurons. In particular, we demonstrate that there exists an optimal modularity for memory performance, where a balance between local cohesion and global connectivity is established, allowing optimally modular networks to remember longer. Our results suggest that insights from dynamical analysis of neural networks and information-spreading processes can be leveraged to better design neural networks and may shed light on the brain’s modular organization.

Coevolution of Vaccination Opinions and Awareness Affecting the Spread of Epidemics

During the last decades, vaccines have been widely adopted to control or eliminate epidemics. Nevertheless, in practice, a vaccine is usually not completely effective, i.e., there may exist some side effects, which cause individuals to possess different opinions regarding vaccination. Thus, opinion transformation plays an important role in determining the range of epidemic infection. Individuals might be aware of risk due to the process of information spreading, which further affects the spread of epidemics. Moreover, awareness seems to affect the opinion transition process in turn. Motivated by these facts, we investigate the coevolution of vaccination opinion and awareness in a three-layered complex network. The opinion dynamics regarding vaccination evolves on the virtual layer, and the information also spreads on the same layer (for simplicity, these two processes are supported by the same topology). In contrast, biological elements carrying the viruses are transmitted through bodily contact on the physical layer. Furthermore, theoretical analyses through Markov chain are also provided for validation. The corresponding experimental analyses are then performed to investigate the effects of spread or loss of awareness on vaccination rate or epidemic size, and the results are presented and discussed.

Modelo de vigilancia tecnológica para la gestión de un grupo de investigación en salud

Introducción. La dinámica constante de la ciencia, la tecnología y la innovación, con el volumen de información disponible, constituyen un reto para el quehacer de los grupos de investigación en su compromiso como unidades generadoras de resultados de conocimiento y de aportes relevantes a su entorno. Una alternativa a la planeación que dé soporte a la toma de decisiones con mayor conocimiento, menor riesgo y oportuna anticipación a los cambios debería estar soportada en un proceso organizado, selectivo y sistemático como lo es la vigilancia tecnológica. Objetivo. Revisión de literatura de tipo descriptivo sobre la vigilancia tecnológica y generación de una propuesta de un modelo de vigilancia tecnológica para la gestión de la actividad científica del grupo de investigación Estudio Genético de Enfermedades Complejas (EGEC). Metodología. Revisión de la literatura de tipo descriptivo. Se consultaron las bases de datos de Medline, SciELO, Ebsco e IEEE y el buscador Google Scholar, mediante la combinación de las palabras clave “vigilancia tecnológica/technological surveillance”, “salud/health”, “enfermedad/disease”, “ejemplos en salud”, “universidad” e “investigación”. Se incluyeron artículos científicos de los últimos cinco años y se excluyeron artículos que solo consideraran la difusión de resultados de ejercicios de vigilancia tecnológica. Con base en el análisis de los resultados, se propone un modelo de vigilancia tecnológica para la gestión de la actividad científica del grupo de investigación EGEC, para lo que se estructuró el diagrama de las etapas, insumos y recursos que integran el modelo propuesto, incluyendo como estrategia de validación el desarrollo de una prueba piloto sobre cáncer, a partir de la cual se hicieron ajustes adicionales al mismo. Resultados. Como referente internacional se encuentra la Norma UNE 166006:2011 de la Asociación Española de Normalización y Certificación (AENOR) y, como referente nacional, España y sus Regiones Intercambian Conocimiento con Antioquia (ERICA) e Innovaciones a partir de la Vigilancia Tecnológica (InnoViTech). Se establecen las etapas conceptuales, buenas prácticas para el ejercicio y recursos para el desarrollo de la vigilancia tecnológica. Los principales componentes del modelo propuesto incluyen la identificación de necesidades, planeación, recolección, auditoría, análisis, documentación y comunicación de información. Conclusiones. El modelo planteado es una base para el desarrollo de una estrategia de gestión del conocimiento de un grupo de investigación y plantea la importancia de escalar hacia el uso de la inteligencia competitiva al interior del mismo. [CarrilloZambrano E, Páez-Leal MC, Suárez JM, Luna-González ML. Modelo de vigilancia tecnológica para la gestión de un grupo de investigación en salud. MedUNAB. 2018;21(1): 84-99. doi: 10.29375/01237047.2746].