Spectral density-based clustering algorithms for complex networks.

ABSTRACT:

Introduction

Clustering is usually the first exploratory analysis step in empirical data. When the data set comprises graphs, the most common approaches focus on clustering its vertices. In this work, we are interested in grouping networks with similar connectivity structures together instead of grouping vertices of the graph. We could apply this approach to functional brain networks (FBNs) for identifying subgroups of people presenting similar functional connectivity, such as studying a mental disorder. The main problem is that real-world networks present natural fluctuations, which we should consider.

Methods

In this context, spectral density is an exciting feature because graphs generated by different models present distinct spectral densities, thus presenting different c

SUBMITTER: Ramos TC 

PROVIDER: S-EPMC10101435 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature