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Message Spreading Model over Online Social Network with Multiple Channels and Multiple Groups

by Sungmin Hwang, Kyungbaek Kim - Proc. the Ninth International Conference on Internet and Web Applications and Services (ICIW 2014 , 2014
"... Abstract—Understanding the characteristics of message spreading over online social network is important for estimating the influence of message initiated from arbitrary users. Past researches present some models, such as independent cascade model and linear threshold model, to explain the message sp ..."
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Abstract—Understanding the characteristics of message spreading over online social network is important for estimating the influence of message initiated from arbitrary users. Past researches present some models, such as independent cascade model and linear threshold model, to explain the message spreading. Recent studies show many variations of previous models focused on different issues. In this paper, we focus on multiple channels that are used for communicating with each other, and multiple groups that react differently to the message coming from each channel, in order to observe a more detailed aspect of message spreading, such as spreading speed or chances to accept the message. Considering these properties, we propose a new message spreading model that has multiple member groups and multiple channels. We examine the impact of channel and group preference in message spreading by conducting extensive simulations of our suggested model. Through the simulations, we observed that considering multiple channels and multiple groups explains the speed and the coverage of message spreading in more detail. Keywords-Message Spreading; Online Social networks; Multiple Channels; Multiple Groups.
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Beyond Models: Forecasting Complex Network Processes Directly from Data

by Bruno Ribeiro, Minh X. Hoang, Ambuj K. Singh
"... Complex network phenomena – such as information cascades in online social networks – are hard to fully observe, model, and forecast. In forecasting, a recent trend has been to forgo the use of parsimonious models in favor of models with in-creasingly large degrees of freedom that are trained to lear ..."
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Complex network phenomena – such as information cascades in online social networks – are hard to fully observe, model, and forecast. In forecasting, a recent trend has been to forgo the use of parsimonious models in favor of models with in-creasingly large degrees of freedom that are trained to learn the behavior of a process from historical data. Extrapolat-ing this trend into the future, eventually we would renounce models all together. But is it possible to forecast the evo-lution of a complex stochastic process directly from the data without a model? In this work we show that model-free fore-casting is possible. We present SED, an algorithm that fore-casts process statistics based on relationships of statistical equivalence using two general axioms and historical data. To the best of our knowledge, SED is the first method that can perform axiomatic, model-free forecasts of complex stochas-tic processes. Our simulations using simple and complex evolving processes and tests performed on a large real-world dataset show promising results.
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The Diffusion of Viral Content in Multi-layered Social Networks

by Jarosław Jankowski , Michał Kozielski , Wojciech Filipowski , Radosław Michalski , 2013
"... Abstract. Modelling the diffusion of information is one of the key areas related to activity within social networks. In this field, there is recent research associated with the use of community detection algorithms and the analysis of how the structure of communities is affecting the spread of info ..."
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Abstract. Modelling the diffusion of information is one of the key areas related to activity within social networks. In this field, there is recent research associated with the use of community detection algorithms and the analysis of how the structure of communities is affecting the spread of information. The purpose of this article is to examine the mechanisms of diffusion of viral content with particular emphasis on cross community diffusion.
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