# Error-Correcting Decoders for Communities in Networks

## TL;DR

This paper introduces a novel community detection method inspired by error-correcting decoders, simplifying the algorithm to achieve linear time complexity while maintaining satisfactory performance on artificial and real networks.

## Contribution

The paper presents a new community detection algorithm based on error-correcting decoding principles, with a simplified version that reduces complexity to linear time.

## Key findings

- The simplified algorithm maintains competitive accuracy.
- The method performs well on artificial benchmarks.
- Effective on real-world network data.

## Abstract

As recent work demonstrated, the task of identifying communities in networks can be considered analogous to the classical problem of decoding messages transmitted along a noisy channel. We leverage this analogy to develop a community detection method directly inspired by a standard and widely-used decoding technique. We further simplify the algorithm to reduce the time complexity from quadratic to linear. We test the performance of the original and reduced versions of the algorithm on artificial benchmarks with pre-imposed community structure, and on real networks with annotated community structure. Results of our systematic analysis indicate that the proposed techniques are able to provide satisfactory results.