# Fast consensus clustering in complex networks

## TL;DR

This paper introduces a fast consensus clustering algorithm for complex networks that reduces computational complexity to linear, enabling community detection on large-scale graphs with millions of nodes and links.

## Contribution

The authors propose a novel linear-time consensus clustering method that approximates the consensus matrix using only original links and a small set of node pairs, maintaining accuracy.

## Key findings

- Complexity reduced to linear time.
- Performance comparable to traditional methods.
- Applicable to networks with millions of nodes.

## Abstract

Algorithms for community detection are usually stochastic, leading to different partitions for different choices of random seeds. Consensus clustering has proven to be an effective technique to derive more stable and accurate partitions than the ones obtained by the direct application of the algorithm. However, the procedure requires the calculation of the consensus matrix, which can be quite dense if (some of) the clusters of the input partitions are large. Consequently, the complexity can get dangerously close to quadratic, which makes the technique inapplicable on large graphs. Here we present a fast variant of consensus clustering, which calculates the consensus matrix only on the links of the original graph and on a comparable number of additional node pairs, suitably chosen. This brings the complexity down to linear, while the performance remains comparable as the full technique. Therefore, our fast consensus clustering procedure can be applied on networks with millions of nodes and links.