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arXiv 1905.04416physics.soc-phcond-mat.stat-mech
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多个交互传播过程中的竞争、协作与优化

Competition, Collaboration, and Optimization in Multiple Interacting Spreading Processes

Hanlin Sun, David Saad, Andrey Y. Lokhov

摘要:

竞争和合作是多智能体概率传播过程的核心。舆论战和竞争性营销活动是前者的典型例子,而艾滋病毒和结核病等多种疾病的共同传播则表明了后者。这些传播受到底层网络拓扑结构、网络组成部分之间的感染率、恢复率以及同样重要的传播过程本身之间的相互作用的影响。在这里,我们第一次推导出动态的消息传递方程,提供了一个精确的描述动态的两个相互作用的传播过程的树图,并开发系统的低复杂度模型,预测一般图上的传播。我们还开发了一个理论框架,通过预算约束下的优化资源分配,并在有限的时间窗口内的相互作用的传播过程的最优控制。衍生算法可用于在存在竞争对手竞争过程的情况下最大化所需的传播,并在偶联传染病的情况下通过疫苗接种限制传播。我们证明了框架和优化方法在合成和现实世界的网络上的有效性。

英文摘要:

Competition and collaboration are at the heart of multi-agent probabilistic spreading processes. The battle on public opinion and competitive marketing campaigns are typical examples of the former, while the joint spread of multiple diseases such as HIV and tuberculosis demonstrates the latter. These spreads are influenced by the underlying network topology, the infection rates between network constituents, recovery rates and, equally importantly, the interactions between the spreading processes themselves. Here, for the first time we derive dynamic message-passing equations that provide an exact description of the dynamics of two interacting spreading processes on tree graphs, and develop systematic low-complexity models that predict the spread on general graphs. We also develop a theoretical framework for an optimal control of interacting spreading processes through an optimized resource allocation under budget constraints and within a finite time window. Derived algorithms can be used to maximize the desired spread in the presence of a rival competitive process, and to limit the spread through vaccination in the case of coupled infectious diseases. We demonstrate the efficacy of the framework and optimization method on both synthetic and real-world networks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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