# Z-score-based modularity for community detection in networks

arXiv: https://arxiv.org/abs/1501.01909 · HTML: https://mlsanity.com/paper/1501.01909/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2015.

## Built on by (most cited first, 2 of 2)

- [DCC: A Cascade based Approach to Detect Communities in Social Networks](https://mlsanity.com/paper/2212.10937/) (2022)
- [Sifting out communities in large sparse networks](https://mlsanity.com/paper/2405.00816/) (2024)

## Abstract (arXiv)

Identifying community structure in networks is an issue of particular interest in network science. The modularity introduced by Newman and Girvan [Phys. Rev. E 69, 026113 (2004)] is the most popular quality function for community detection in networks. In this study, we identify a problem in the concept of modularity and suggest a solution to overcome this problem. Specifically, we obtain a new quality function for community detection. We refer to the function as Z-modularity because it measures the Z-score of a given division with respect to the fraction of the number of edges within communities. Our theoretical analysis shows that Z-modularity mitigates the resolution limit of the original modularity in certain cases. Computational experiments using both artificial networks and well-known real-world networks demonstrate the validity and reliability of the proposed quality function.