广义网络中用户兴趣决定的信息扩散分析与控制。

Q1 Mathematics
Computational Social Networks Pub Date : 2015-01-01 Epub Date: 2015-12-02 DOI:10.1186/s40649-015-0025-4
Eleni Stai, Vasileios Karyotis, Symeon Papavassiliou
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引用次数: 7

摘要

广义网络中有用信息的扩散,例如由无线物理基板和社会网络覆盖组成的网络,对于理论和实际应用都非常重要。与以前的工作相反,我们专注于用户兴趣及其特征(例如,兴趣周期性)对这种复杂的无线社会系统中有用信息扩散的动态和控制的影响。通过考虑用户兴趣的时间和主题变化的影响,例如,在春夏期间传播更有效的暑假广告的兴趣的季节性周期性,我们开发了一个基于流行病的数学框架来建模和分析这些信息传播过程,并使用三个指示性操作场景来演示通过相应的基于微分方程的形式主义可以获得的解决方案和结果。然后,我们根据上述信息扩散模型开发了一个最优控制框架,通过考虑和利用用户兴趣对扩散过程的影响,该框架允许控制信息传播效率和相关成本之间的权衡。通过分析和广泛的模拟,得到了各网络层和相关兴趣参数对有用信息扩散动态的影响的重要结果。此外,通过分析和仿真验证了有用信息扩散最优控制与感染/知情节点数量和用户兴趣演变相关的几个行为特性。具体来说,一个关键的发现是低利息相关的扩散可以通过利用适当的最优控制来辅助。我们在本文中的工作为这种以用户为中心的信息扩散框架铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analysis and control of information diffusion dictated by user interest in generalized networks.

Analysis and control of information diffusion dictated by user interest in generalized networks.

Analysis and control of information diffusion dictated by user interest in generalized networks.

Analysis and control of information diffusion dictated by user interest in generalized networks.

The diffusion of useful information in generalized networks, such as those consisting of wireless physical substrates and social network overlays is very important for theoretical and practical applications. Contrary to previous works, we focus on the impact of user interest and its features (e.g., interest periodicity) on the dynamics and control of diffusion of useful information within such complex wireless-social systems. By considering the impact of temporal and topical variations of users interests, e.g., seasonal periodicity of interest in summer vacation advertisements which spread more effectively during Spring-Summer months, we develop an epidemic-based mathematical framework for modeling and analyzing such information dissemination processes and use three indicative operational scenarios to demonstrate the solutions and results that can be obtained by the corresponding differential equation-based formalism. We then develop an optimal control framework subject to the above information diffusion modeling that allows controlling the trade-off between information propagation efficiency and the associated cost, by considering and leveraging on the impact that user interests have on the diffusion processes. By analysis and extensive simulations, significant outcomes are obtained on the impact of each network layer and the associated interest parameters on the dynamics of useful information diffusion. Furthermore, several behavioral properties of the optimal control of the useful information diffusion with respect to the number of infected/informed nodes and the evolving user interest are shown through analysis and verified via simulations. Specifically, a key finding is that low interest-related diffusion can be aided by utilizing proper optimal controls. Our work in this paper paves the way towards this user-centered information diffusion framework.

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来源期刊
Computational Social Networks
Computational Social Networks Mathematics-Modeling and Simulation
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.
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