论文标题
自适应随机步行的最大分散
Maximal dispersion of adaptive random walks
论文作者
论文摘要
最大的熵随机步行（MERW）是最大程度地分散的，并且在优化各种情况下的信息传播方面发挥了关键作用。但是，构建Merws是以事先了解网络结构为代价的，这一要求使它们在实际情况下完全不足。在这里，我们提出了一个自适应随机步行（ARW），该步行将通过在探索网络时对收集的本地信息更新其过渡规则来最大化分散。我们展示了如何通过MERW的大型传播表示来得出ARW，并研究其在合成和现实世界网络上的动态。
Maximum entropy random walks (MERWs) are maximally dispersing and play a key role in optimizing information spreading in various contexts. However, building MERWs comes at the cost of knowing beforehand the global structure of the network, a requirement that makes them totally inadequate in real case scenarios. Here, we propose an adaptive random walk (ARW), which instead maximizes dispersion by updating its transition rule on the local information collected while exploring the network. We show how to derive ARW via a large-deviation representation of MERW and study its dynamics on synthetic and real world networks.
