Classical and Quantum Random-Walk Centrality Measures in Multilayer Networks

Multilayer network analysis is a useful approach for studying the structural properties of entities with diverse, multitudinous relations. Classifying importance nodes and node-layer tuples an important aspect study multilayer networks. To do this, it common to calculate various centrality measures, which allow one rank node-layers according variety features. In this paper, we formulate occupation, PageRank, betweenness, closeness centralities in terms node-occupation different types continuous-time classical quantum random walks on We apply our framework synthetic real-world networks, identify marked differences between measures. Our computations also give insights into correlations certain random-walk-based geodesic-path-based centralities.