# Efficient method for estimating the number of communities in a network

## TL;DR

This paper introduces an efficient Bayesian-based method for estimating the number of communities in a network, capable of handling diverse and complex network structures accurately.

## Contribution

It presents a novel prior and Monte Carlo sampling scheme for community number estimation, improving upon existing methods.

## Key findings

- Accurately estimates community numbers in diverse networks
- Performs well on both real and synthetic data
- Handles varying community sizes and structures

## Abstract

While there exist a wide range of effective methods for community detection in networks, most of them require one to know in advance how many communities one is looking for. Here we present a method for estimating the number of communities in a network using a combination of Bayesian inference with a novel prior and an efficient Monte Carlo sampling scheme. We test the method extensively on both real and computer-generated networks, showing that it performs accurately and consistently, even in cases where groups are widely varying in size or structure.