Counting the number of metastable states in the modularity landscape: Algorithmic detectability limit of greedy algorithms in community detection

TL;DR

This paper analyzes the limitations of greedy modularity maximization algorithms in community detection by estimating the number of metastable states, revealing the sparsity levels where these algorithms tend to fail.

Contribution

It provides a quantitative estimate of the algorithmic performance limit of greedy modularity maximization through counting metastable states.

Findings

Identifies the sparsity threshold where greedy algorithms typically fail

Quantifies the number of metastable states in the modularity landscape

Offers insights into the failure modes of greedy community detection methods

Abstract

Modularity maximization using greedy algorithms continues to be a popular approach toward community detection in graphs, even after various better forming algorithms have been proposed. Apart from its clear mechanism and ease of implementation, this approach is persistently popular because, presumably, its risk of algorithmic failure is not well understood. This Rapid Communication provides insight into this issue by estimating the algorithmic performance limit of modularity maximization. This is achieved by counting the number of metastable states under a local update rule. Our results offer a quantitative insight into the level of sparsity at which a greedy algorithm typically fails.