# Discovering Communities of Community Discovery

## TL;DR

This paper introduces an empirical, similarity-based classification of community detection algorithms by creating an Algorithm Similarity Network, which groups algorithms based on the similarity of their results across numerous networks, aiding practitioners in selecting suitable methods.

## Contribution

It proposes a novel similarity-based classification of community detection algorithms using an Algorithm Similarity Network derived from empirical comparisons of over seventy approaches.

## Key findings

- The ASN contains well-separated groups of algorithms.
- Similarity-based classification improves understanding of algorithm relationships.
- The approach aids practitioners in choosing appropriate community detection methods.

## Abstract

Discovering communities in complex networks means grouping nodes similar to each other, to uncover latent information about them. There are hundreds of different algorithms to solve the community detection task, each with its own understanding and definition of what a "community" is. Dozens of review works attempt to order such a diverse landscape -- classifying community discovery algorithms by the process they employ to detect communities, by their explicitly stated definition of community, or by their performance on a standardized task. In this paper, we classify community discovery algorithms according to a fourth criterion: the similarity of their results. We create an Algorithm Similarity Network (ASN), whose nodes are the community detection approaches, connected if they return similar groupings. We then perform community detection on this network, grouping algorithms that consistently return the same partitions or overlapping coverage over a span of more than one thousand synthetic and real world networks. This paper is an attempt to create a similarity-based classification of community detection algorithms based on empirical data. It improves over the state of the art by comparing more than seventy approaches, discovering that the ASN contains well-separated groups, making it a sensible tool for practitioners, aiding their choice of algorithms fitting their analytic needs.