# NEDindex: A new metric for community structure in networks

arXiv: https://arxiv.org/abs/1610.03176 · HTML: https://mlsanity.com/paper/1610.03176/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2016.

## Built on by (most cited first, 1 of 1)

- [Concept drift mitigation through community and spectral graph analysis for the detection of cyberattacks in network traffic](https://mlsanity.com/paper/2609.09442/) (2026)

## Abstract (arXiv)

There are several metrics (Modularity, Mutual Information, Conductance, etc.) to evaluate the strength of graph clustering in large graphs. These metrics have great significance to measure the effectiveness and they are often used to find the strongly connected clusters with respect to the whole graph. In this paper, we propose a new metric to evaluate the strength of graph clustering and also study its applications. We show that our proposed metric has great consistency which is similar to other metrics and easy to calculate. Our proposed metric also shows consistency where other metrics fail in some special cases. We demonstrate that our metric has reasonable strength while extracting strongly connected communities in both simulated (in silico) data and real data networks. We also show some comparative results of our proposed metric with other popular metric(s) for Online Social Networks (OSN) and Gene Regulatory Networks (GRN).