# Core of communities in bipartite networks

## TL;DR

This paper presents a method to identify core communities within bipartite networks by analyzing statistically validated projections, demonstrating robustness and high precision in various real-world and artificial networks.

## Contribution

It introduces a novel approach to detect core communities in bipartite networks using statistically validated projections, enhancing robustness and interpretability.

## Key findings

- Cores are highly informative and robust to errors.
- Method achieves high precision in core detection.
- Accuracy varies depending on network conditions.

## Abstract

We use the information present in a bipartite network to detect cores of communities of each set of the bipartite system. Cores of communities are found by investigating statistically validated projected networks obtained using information present in the bipartite network. Cores of communities are highly informative and robust with respect to the presence of errors or missing entries in the bipartite network. We assess the statistical robustness of cores by investigating an artificial benchmark network, the co-authorship network, and the actor-movie network. The accuracy and precision of the partition obtained with respect to the reference partition are measured in terms of the adjusted Rand index and of the adjusted Wallace index respectively. The detection of cores is highly precise although the accuracy of the methodology can be limited in some cases.