# Exploring the limits of community detection strategies in complex networks

arXiv: https://arxiv.org/abs/1306.4149 · HTML: https://mlsanity.com/paper/1306.4149/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2013.

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

- [The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks](https://mlsanity.com/paper/2310.01144/) (2023)
- [Module-based regularization improves Gaussian graphical models when observing noisy data](https://mlsanity.com/paper/2303.16796/) (2023)
- [Graph-Based Floor Separation Using Node Embeddings and Clustering of WiFi Trajectories](https://mlsanity.com/paper/2505.08088/) (2025)
- [Reliable data clustering with Bayesian community detection](https://mlsanity.com/paper/2510.15013/) (2025)

## Abstract (arXiv)

The characterization of network community structure has profound implications in several scientific areas. Therefore, testing the algorithms developed to establish the optimal division of a network into communities is a fundamental problem in the field. We performed here a highly detailed evaluation of community detection algorithms, which has two main novelties: 1) using complex closed benchmarks, which provide precise ways to assess whether the solutions generated by the algorithms are optimal; and, 2) A novel type of analysis, based on hierarchically clustering the solutions suggested by multiple community detection algorithms, which allows to easily visualize how different are those solutions. Surprise, a global parameter that evaluates the quality of a partition, confirms the power of these analyses. We show that none of the community detection algorithms tested provide consistently optimal results in all networks and that Surprise maximization, obtained by combining multiple algorithms, obtains quasi-optimal performances in these difficult benchmarks.