Edge-based Katz centralities for spatio-temporal multiplex networks

2026-09-21

AI Summary

本文提出了一种基于边的Katz中心性计算方法，用于时空多层网络，并通过块三角和带状超邻接矩阵表示法来解决网络结构小变化导致中心性大幅变化的问题。

Abstract

Katz centrality is a well-established measure to identify and rank the most important nodes in complex networks by means of a linear system solve. Recent works have developed notions of Katz centrality for temporal, i.e., time-evolving networks. Their drawback is that small changes in the network structure may drastically change centralities across large parts of the network. Moreover, an unproportional effort in the network science community has been devoted to the study of node-based quantities while edge-based measures are far less explored. In this manuscript, we introduce a novel failure-aware temporal multiplex network model for computing edge-based Katz centralities for spatial networks. This class of networks admits the use of the line graph as network representation. We use a block-triangular and -banded supra-adjacency matrix representation, modeling inter-layer connections as low-rank matrices, which assigns a spatially constrained emphasis on up- or downdated edges. These could represent, e.g., breakages of water/gas pipes or obstructions in street networks in real-time infrastructure monitoring applications. We analyze the structure of the of the block matrix inverse with respect to entry decay in the blocks above the block-diagonal. This gives rise to two truncation approaches that allow drastic computational runtime reductions at the cost of introducing a controlled truncation error. Numerical experiments on a range of real-world spatio-temporal networks of size up to $2\cdot 10^8$ illustrate accuracy and efficiency with runtime gains of up to a factor of $679$.

Problem

• Edge-based Katz centrality
• spatio-temporal multiplex networks
• network structure changes
• failure-aware
• real-time infrastructure monitoring

Innovation

• edge-based Katz centrality
• spatio-temporal multiplex networks
• line graph
• block-triangular and -banded supra-adjacency matrix
• truncation approaches