Spectral coarse-graining of complex networks

Abstract

Reducing the complexity of large systems described as complex networks is key to understand them and a crucial issue is to know which properties of the initial system are preserved in the reduced one. Here we use random walks to design a coarse-graining scheme for complex networks. By construction the coarse-graining preserves the slow modes of the walk, while reducing significantly the size and the complexity of the network. In this sense our coarse-graining allows to approximate large networks by smaller ones, keeping most of their relevant spectral properties.

4 pages, 2 figures

References in corpus (3)

• Modularity and community structure in networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Ring structures and mean first passage time in networks

Cited by in corpus (4)

• Extracting the multiscale backbone of complex weighted networks
• Spectral Coarse Graining and Synchronization in Oscillator Networks
• Coarse graining of master equations with fast and slow states
• Detecting groups of similar components in complex networks