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

arXiv: https://arxiv.org/abs/1808.07690 · HTML: https://mlsanity.com/paper/1808.07690/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularity](https://mlsanity.com/paper/2209.04562/) (2022)
- [Analyzing Modularity Maximization in Approximation, Heuristic, and Graph Neural Network Algorithms for Community Detection](https://mlsanity.com/paper/2310.10898/) (2023)
- [Heuristic Modularity Maximization Algorithms for Community Detection Rarely Return an Optimal Partition or Anything Similar](https://mlsanity.com/paper/2302.14698/) (2023)
- [Robust Markov stability for community detection at a scale learned based on the structure](https://mlsanity.com/paper/2504.11621/) (2025)

## Abstract (arXiv)

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.