Information Spreading Process Research Articles

Modeling for information spreading basing on dynamics

SummaryAs a burning issue in today's society, the study on information spreading has great theoretic and practical implications. After analyzing the dynamics of information spreading, characteristics of decrease progressively probability and step change property have been disclosed. Taking these into account, the variable probability Known/Unknown (VPKU) model for information spreading is built with appropriate parameter assumption. Then, simulation experiments are made to evolve the VPKU model and analyze the regularity of information spreading. Furthermore, applying the VPKU model, the control strategy of information spreading is proposed and discussed. Simulation and analysis shows that the VPKU model can reflect the process of information spreading in different situations with different parameter setting.

MM-SIS: Model for multiple information spreading in multiplex network

In social networks, relationships between information are complexly intertwined. However, prior research mainly focuses on studying the independent information in an isolated network, ignoring two factors: the interaction between different information and the diversity of propagation paths. In view of the above problems, this paper proposes the MM-SIS(Multiple information and Multiplex network-SIS)model to explore the detailed processes and characteristics of multiple information in multiplex networks. At the same time, the concept of influence factor is introduced to describe the complex interaction between different information. Moreover, we use the Microscopic Markov Chain method to set dynamic equations, theoretically analyze the epidemic threshold and verify its correctness through experiments. In general, this paper mainly explores the effects of influence factor and the interrelation of network layers on information spreading process. The experimental results show that the above mentioned two factors have obvious impacts on the information outbreak scale and the epidemic threshold.

The competitive information spreading over multiplex social networks

It is evident that social networks have become major information sources as well as the most effective platform for information exchange. In social networks, the dynamical propagation of different information often exhibits different dynamical behaviors. And different information may even compete with each other that can determine the dynamical process of information dissemination. These phenomena have led to the present study on the spreading process of competitive information over social networks. In this study, we proposed a competitive information model over the multiplex networks. The simulations of this model are verified by two types of the multiplex networks, such as the real composite network and the artificial composite network. Through controlling the spreading parameters in extensive large-scale simulations, it is found that the final density of stiflers increases with the growth of the spreading rate, while it declines with the increasing of the removal rate. It is also found that the spreading process of the competitive information is closely related to the node degrees on multiplex networks. Through controlling the exchanging rate of competitive information, we are able to determine information dominance accurately. Our new findings validate that the proposed model is capable of characterizing the dynamic evolution of competitive information over multiplex social networks. The results of this study are significant to the study of social science and social platform behavior.

Stability under stochastic perturbation of solutions of mathematical models of information spreading process with external control

Stability under stochastic perturbation of solutions of mathematical models of information spreading process with external control

Can co-location be used as a proxy for face-to-face contacts?

Technological advances have led to a strong increase in the number of data collection efforts aimed at measuring co-presence of individuals at different spatial resolutions. It is however unclear how much co-presence data can inform us on actual face-to-face contacts, of particular interest to study the structure of a population in social groups or for use in data-driven models of information or epidemic spreading processes. Here, we address this issue by leveraging data sets containing high resolution face-to-face contacts as well as a coarser spatial localisation of individuals, both temporally resolved, in various contexts. The co-presence and the face-to-face contact temporal networks share a number of structural and statistical features, but the former is (by definition) much denser than the latter. We thus consider several down-sampling methods that generate surrogate contact networks from the co-presence signal and compare them with the real face-to-face data. We show that these surrogate networks reproduce some features of the real data but are only partially able to identify the most central nodes of the face-to-face network. We then address the issue of using such down-sampled co-presence data in data-driven simulations of epidemic processes, and in identifying efficient containment strategies. We show that the performance of the various sampling methods strongly varies depending on context. We discuss the consequences of our results with respect to data collection strategies and methodologies.

True and fake information spreading over the Facebook

Social networks have involved more and more users who search for and share information extensively and frequently. Tremendous evidence in Facebook, Twitter, Flickr and Google+ alike shows that such social networks are the major information sources as well as the most effective platforms for information transmission and exchange. The dynamic propagation of various information may gradually disseminate, drastically increase, strongly compete with each other, or slowly decrease. These observations had led to the present study of the spreading process of true and fake information over social networks, particularly the Facebook. Specifically, in this paper the topological structure of two huge-scale Facebook network datasets are investigated regarding their statistical properties. Based on that, an information model for simulating the true and fake information spreading over the Facebook is established. Through controlling the spreading parameters in extensive large-scale simulations, it is found that the final density of stiflers increases with the growth of the spreading rate, while it would decline with the increase of the removal rate. Moreover, it is found that the spreading process of the true–fake information is closely related to the node degrees on the network. Hub-individuals with high degrees have large probabilities to learn hidden information and then spread it. Interestingly, it is found that the spreading rate of the true information but not of the fake information has a great effect on the information spreading process, reflecting the human nature in believing and spreading truths in social activities. The new findings validate the proposed model to be capable of characterizing the dynamic evolution of true and fake information over the Facebook, useful and informative for future social science studies.

Information spreading dynamics in hypernetworks

Contact pattern and spreading strategy fundamentally influence the spread of information. Current mathematical methods largely assume that contacts between individuals are fixed by networks. In fact, individuals are affected by all his/her neighbors in different social relationships. Here, we develop a mathematical approach to depict the information spreading process in hypernetworks. Each individual is viewed as a node, and each social relationship containing the individual is viewed as a hyperedge. Based on SIS epidemic model, we construct two spreading models. One model is based on global transmission, corresponding to RP strategy. The other is based on local transmission, corresponding to CP strategy. These models can degenerate into complex network models with a special parameter. Thus hypernetwork models extend the traditional models and are more realistic. Further, we discuss the impact of parameters including structure parameters of hypernetwork, spreading rate, recovering rate as well as information seed on the models. Propagation time and density of informed nodes can reveal the overall trend of information dissemination. Comparing these two models, we find out that there is no spreading threshold in RP, while there exists a spreading threshold in CP. The RP strategy induces a broader and faster information spreading process under the same parameters.

Predictive estimation of mathematical models of information spreading process under uncertainty

The mathematical model of spreading the information of an arbitrary number of types was considered. The model takes the form of a system of non-linear ordinary differential equations with stationary parameters. Special cases of presenting observation errors are considered. The algorithms for building averaged optimal rms predictive estimation and guaranteed predictive estimation are offered. The algorithm for building averaged optimal rms predictive estimation for a case of spreading information of one type and the algorithm for finding guaranteed predictive estimation for a particular case of representing a set of possible observation errors are obtained. The results of a numerical experiment for the problem of building guaranteed predictive estimates for the system with two sources of information are considered.

Seeking powerful information initial spreaders in online social networks: a dense group perspective

The rapid growth of online social networks (OSNs) has ultimately facilitated information spreading and changed the economics of mobile networks. It is important to understand how to spread information as widely as possible. In this paper, we aim to seek powerful information initial spreaders with an efficient manner. We use the mean-field theory to characterize the process of information spreading based on the Susceptible Infected (SI) model and validate that the prevalence of information depends on the network density. Inspired by this result, we seek the initial spreaders from closely integrated groups of nodes, i.e., dense groups (DGs). In OSNs, DGs distribute dispersedly over the network, so our approach can be fulfilled in a distributed way by seeking the spreaders in each DG. We first design a DG Generating Algorithm to detect DGs, where nodes within the DG have more internal connections than external ones. Second, based on the detected DGs, we design a criterion to seek powerful initial spreaders from each DG. We conduct experiments as well as statistical analysis on real OSNs. The results show that our approach provides a satisfactory performance as well as computational efficiency.

PEMANFAATAN WEBSITE RAHARJA.AC.ID SEBAGAI MEDIA INFORMASI DAN UPLOAD ARTIKEL UNTUK NS-CCIT MENGGUNAKAN FRAMEWORK YII

The telecommunication world’s development, who’s keep on moving rapidly have create a developed innovation and creativity to also keep on moving along with the technological information’s development. Now, in Raharja University, a new program has been born which is called NSCCIT, its a National Seminar and Call for Paper who’s now have been built as a place to discuss the result of a research through a seminar. Seminar is a scheduled forum of a group of people to scientifically discuss about a topic with the purpose to grow and to manage the skills, scientific act and professionalism act through a more objective understanding about a topic which is going to be evaluated. With the creation of the NSCCIT’'s research or national seminar and Call of Paper, the result is expected to be a best solution for the civitas academica to communicate and discuss the problems which is going to be researched and help each other in solving the problems. The main purpose of this research is to develop a web database based system on raharja.ac.id website to make the spreading process of seminar's schedule information more easily using the PHP and MySQL’s technology and also implemented as an information’s conveyor media about the seminar’s schedule which is going to be held.

Modeling for Information Diffusion in Online Social Networks via Hydrodynamics

In recent years, online social networks have gained tremendous popularity because of the massive number of online users, the fast spread of information, and strong inter-personal influence. However, due to the high complexity of the user interaction and the real-time changing of the online social networks, it is still a big challenge to model the spreading process of the information delicately and, then, to predict the information diffusion precisely. In this paper, we exploit a hydrodynamic model to describe the spreading process of the information in online social networks. By using the proposed hydrodynamic information diffusion prediction model (hydro-IDP), we can describe the spreading process of the information on both temporal and spatial perspectives. It is also helpful in extracting the characteristics of information diffusion (e.g., the information popularity, the user influence, and the diffusivity of social platform). We also consider the superimposed effect of the information diffusion resulted from influential users in the model. The high accuracy of the model results has illustrated that our proposed Hydro-IDP model is competent to describe and predict the spreading process of information in online social networks.

Optimal Control Theory-Based Epidemic Information Spreading Scheme for Mobile Social Users With Energy Constraint

With the rapid development of energy-efficient cloud computing technologies, mobile users can share information with each other in different communities. However, due to the ever-increasing population of mobile users and the scale of networks, the energy-efficient delivery of information in mobile social networks (MSNs) becomes a new challenge. Therefore, in this paper, we propose an optimal control theory-based epidemic information spreading scheme for mobile users with energy constraint. First, we divide the social relationships of mobile users into four types based on blood, geography, work, and interest relationship. Second, we develop an analytical model to evaluate the influences of social relationships on the information spreading process in MSNs. Third, we present a control theory-based information spreading scheme to optimize the tradeoff between the consumed energy and delay. Finally, numerical results demonstrate that the presented scheme can save the energy and improve the quality of experience during information spreading for mobile users.

Information spreading on mobile communication networks: A new model that incorporates human behaviors

Abstract Recently, there is a growing interest in the modeling and simulation based on real social networks among researchers in multi-disciplines. Using an empirical social network constructed from the calling records of a Chinese mobile service provider, we here propose a new model to simulate the information spreading process. This model takes into account two important ingredients that exist in real human behaviors: information prevalence and preferential spreading. The fraction of informed nodes when the system reaches an asymptotically stable state is primarily determined by information prevalence, and the heterogeneity of link weights would slow down the information diffusion. Moreover, the sizes of blind clusters which consist of connected uninformed nodes show a power-law distribution, and these uninformed nodes correspond to a particular portion of nodes which are located at special positions in the network, namely at the edges of large clusters or inside the clusters connected through weak links. Since the simulations are performed on a real world network, the results should be useful in the understanding of the influences of social network structures and human behaviors on information propagation.

Spatial Correlation in Expected Returns in Commercial Real Estate Markets and the Role of Core Markets

This study is based on survey data on investor expectations for 40 metropolitan statistical areas (MSAs) in the US over the period from 2003:Q2 to 2014:Q2. The paper has two main objectives. These are firstly to identify whether expected rates of return across different commercial real estate markets are positively spatially correlated and secondly to analyze the role of core markets like New York, Washington, DC, Los Angeles, San Francisco, and Chicago in the information-spreading process. All the tests conducted are conditional on the maintained hypothesis that expected returns across different markets are spatially uncorrelated. To carry out these tests, we regress a measure of excess return in each of the 40 markets on (contemporaneous) measures of expected returns in other markets, controlling for the interaction between (neighboring) markets. We find very large spatial interaction coefficients across different markets. Our basic estimates yield a spatial correlation coefficient of near one and statistically significant at standard levels. Chow tests allow rejection of the hypothesis that core markets like New York, Washington, DC, Los Angeles, San Francisco, and Chicago are no different than markets like Cleveland, Columbus, Orlando, Pittsburgh, Salt Lake City, St. Louis, and Tampa in explaining the covariances of the rates of return among different commercial real estate markets. Tests of structural change also imply that core markets currently play a more prominent role that in the past in the transfer of information from one market to another.

Modeling Dynamics of Online Video Popularity

Large Internet video delivery systems serve millions of videos to tens of millions of users on daily basis, via Video-on-Demand and live streaming. Video popularity evolves over time. It represents the workload, as welll as business value, of the video to the overall system. The ability to predict video popularity is very helpful for improving service quality and operating efficiency. Previous studies adopted simple models for video popularity, or directly adopted patterns from measurement studies. In this paper, we develop a stochastic fluid model that tries to capture two hidden processes that give rise to different patterns of a given video's popularity evolution: the information spreading process, and the user reaction process. Specifically, these processes model how the video is recommended to the user, the videos inherent attractiveness, and users reaction rate, and yield specific popularity evolution patterns. We then validate our model by matching the predictions of the model with observed patterns from our collaborator, a large content provider in China. This model thus gives us the insight to explain the common and different video popularity evolution patterns and why.

Disease Containment Strategies based on Mobility and Information Dissemination.

Human mobility and social structure are at the basis of disease spreading. Disease containment strategies are usually devised from coarse-grained assumptions about human mobility. Cellular networks data, however, provides finer-grained information, not only about how people move, but also about how they communicate. In this paper we analyze the behavior of a large number of individuals in Ivory Coast using cellular network data. We model mobility and communication between individuals by means of an interconnected multiplex structure where each node represents the population in a geographic area (i.e., a sous-préfecture, a third-level administrative region). We present a model that describes how diseases circulate around the country as people move between regions. We extend the model with a concurrent process of relevant information spreading. This process corresponds to people disseminating disease prevention information, e.g., hygiene practices, vaccination campaign notices and other, within their social network. Thus, this process interferes with the epidemic. We then evaluate how restricting the mobility or using preventive information spreading process affects the epidemic. We find that restricting mobility does not delay the occurrence of an endemic state and that an information campaign might be an effective countermeasure.

Information-driven trade and price–volume relationship in artificial stock markets

The positive relation between stock price changes and trading volume (price–volume relationship) as a stylized fact has attracted significant interest among finance researchers and investment practitioners. However, until now, consensus has not been reached regarding the causes of the relationship based on real market data because extracting valuable variables (such as information-driven trade volume) from real data is difficult. This lack of general consensus motivates us to develop a simple agent-based computational artificial stock market where extracting the necessary variables is easy. Based on this model and its artificial data, our tests have found that the aggressive trading style of informed agents can produce a price–volume relationship. Therefore, the information spreading process is not a necessary condition for producing price–volume relationship.

Stability analysis of information spreading on SNS based on refined SEIR model

Investigating the stability of information spreading over SNS helps to understand the principles inherent in the spreading behavior. This paper explores the mechanisms of information spreading including stifling mechanism, latent mechanism and forgetting mechanism, establishes a refined SEIR model, and builds the corresponding mean-field equations. The methods of the differential dynamics and the next generation matrix are used to calculate the equilibriums and the basic reproductive number, and the asymptotical stability of the network equilibriums are proved theoretically. Simulation experiments are carried out to analyze the effect of the spreading mechanisms on the information spreading process and the results support our conclusions.

Probing models of information spreading in social networks

We apply signal processing analysis to the information spreading in a scale-free network. To reproduce typical behaviours obtained from the analysis of information spreading in the World Wide Web, we use a modified SIS (from ‘susceptible–infectious–susceptible’) model where synergy effects and influential nodes are taken into account. This model depends on a single free parameter that characterizes the memory time of the spreading process. We show that by means of fractal analysis it is possible—from aggregated easily accessible data—to gain information on the memory time of the underlying mechanism driving the information spreading process.

Influence of reciprocal links in social networks.

How does reciprocal links affect the function of real social network? Does reciprocal link and non-reciprocal link play the same role? Previous researches haven't displayed a clear picture to us until now according to the best of our knowledge. Motivated by this, in this paper, we empirically study the influence of reciprocal links in two representative real datasets, Sina Weibo and Douban. Our results demonstrate that the reciprocal links play a more important role than non-reciprocal ones in information diffusion process. In particular, not only coverage but also the speed of the information diffusion can be significantly enhanced by considering the reciprocal effect. We give some possible explanations from the perspectives of network connectivity and efficiency. This work may shed some light on the in-depth understanding and application of the reciprocal effect in directed online social networks.