论文标题
多层网络中的经典和量子随机步行中心度度量
Classical and quantum random-walk centrality measures in multilayer networks
论文作者
论文摘要
多层网络分析是研究具有多种多样的，众多关系的实体的结构特性的有用方法。分类节点和节点层元素的重要性是多层网络研究的重要方面。为此，通常可以根据各种结构特征来计算各种中心度度量，从而使人们可以根据各种结构特征对节点和节点层进行排名。在本文中，我们根据多层网络上不同类型的连续时间经典和量子随机步行的节点占用性能来制定职业，pagerank，pagerank，pagerank，中间和亲密的中心。我们将框架应用于各种合成和现实世界的多层网络，并确定经典和量子中心度量之间的明显差异。我们的计算还可以深入了解某些基于随机步行和基于地球路径的中心之间的相关性。
Multilayer network analysis is a useful approach for studying the structural properties of entities with diverse, multitudinous relations. Classifying the importance of nodes and node-layer tuples is an important aspect of the study of multilayer networks. To do this, it is common to calculate various centrality measures, which allow one to rank nodes and node-layers according to a variety of structural features. In this paper, we formulate occupation, PageRank, betweenness, and closeness centralities in terms of node-occupation properties of different types of continuous-time classical and quantum random walks on multilayer networks. We apply our framework to a variety of synthetic and real-world multilayer networks, and we identify marked differences between classical and quantum centrality measures. Our computations also give insights into the correlations between certain random-walk-based and geodesic-path-based centralities.
