# Spectra of networks containing short loops

## TL;DR

This paper introduces a message passing approach to compute the spectra of networks with many short loops, addressing limitations of existing methods that assume locally tree-like structures.

## Contribution

It develops a novel method for analyzing the spectra of networks containing short loops, expanding the applicability of spectral analysis to more realistic network models.

## Key findings

- Effective computation of spectra in networks with short loops
- Application to various classes of networks
- Addresses limitations of tree-like assumptions

## Abstract

The spectrum of the adjacency matrix plays several important roles in the mathematical theory of networks and in network data analysis, for example in percolation theory, community detection, centrality measures, and the theory of dynamical systems on networks. A number of methods have been developed for the analytic computation of network spectra, but they typically assume that networks are locally tree-like, meaning that the local neighborhood of any node takes the form of a tree, free of short loops. Empirically observed networks, by contrast, often have many short loops. Here we develop an approach for calculating the spectra of networks with short loops using a message passing method. We give example applications to some previously studied classes of networks.