# Generalized Measures for the Evaluation of Community Detection Methods

arXiv: https://arxiv.org/abs/1303.5441 · HTML: https://mlsanity.com/paper/1303.5441/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2013.

## Built on by (most cited first, 3 of 3)

- [Characterizing and comparing external measures for the assessment of cluster analysis and community detection](https://mlsanity.com/paper/2102.00708/) (2021)
- [A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection](https://mlsanity.com/paper/2112.04388/) (2021)
- [Overcoming Bias in Community Detection Evaluation](https://mlsanity.com/paper/2102.03472/) (2021)

## Abstract (arXiv)

Community detection can be considered as a variant of cluster analysis applied to complex networks. For this reason, all existing studies have been using tools derived from this field when evaluating community detection algorithms. However, those are not completely relevant in the context of network analysis, because they ignore an essential part of the available information: the network structure. Therefore, they can lead to incorrect interpretations. In this article, we review these measures, and illustrate this limitation. We propose a modification to solve this problem, and apply it to the three most widespread measures: purity, Rand index and normalized mutual information (NMI). We then perform an experimental evaluation on artificially generated networks with realistic community structure. We assess the relevance of the modified measures by comparison with their traditional counterparts, and also relatively to the topological properties of the community structures. On these data, the modified NMI turns out to provide the most relevant results.