Identification of node rankings in temporal networks based on multilayer topological overlap coefficients

Abstract

Identifying node ranking in complex networks over time is a crucial research topic. The topology relationship of general network nodes reflects their importance in the network. The node ranking evolution within the temporal layers depends not only on the current layer's topology relationship but also on the nodes' interaction relationships as they evolve. In this study, we propose a method called the multilayer topological overlap coefficient-based supra-adjacency matrix to identify node rankings. To account for the node evolution process, we analyze and establish the node ranking matrix structure of unweighted and weighted temporal networks in the temporal network. We also analyze the sequence multilayer node topological overlap structure throughout the whole-time layer. The experimental results demonstrate that the topological overlap coefficient unweighted supra-adjacency matrix of multilayer nodes performs up to 15.00% and 25.80% better than the two supra-adjacency matrix metrics under three different metrics. Moreover, the topological overlap coefficient weighted supra-adjacency matrix of multilayer nodes outperforms the SAM metrics by up to 70.20%.

Stochastic synchronization of Kuramoto-oscillator network with pinning control

“…Complex networks are mathematical models consisting of multiple dynamical systems, describing the relationships of the components in the system, and are widely used in computer science, sociology, biology, and power grid control [1][2][3]. The mathematical principles behind complex networks can be better understood from the perspective of network dynamics.…”