Extraction of hidden information by efficient community detection in networks

TL;DR

This paper presents a new method for detecting optimal community structures in networks and demonstrates how this information can be used to extract hidden data, outperforming existing methods.

Contribution

It introduces a novel approach to identify community structures via modularity and leverages this to uncover hidden information in networks.

Findings

Outperforms current state-of-the-art methods on protein-protein interaction networks.

Effective in extracting hidden information using community detection.

Applicable to various types of networks.

Abstract

Currently, we are overwhelmed by a deluge of experimental data, and network physics has the potential to become an invaluable method to increase our understanding of large interacting datasets. However, this potential is often unrealized for two reasons: uncovering the hidden community structure of a network, known as community detection, is difficult, and further, even if one has an idea of this community structure, it is not a priori obvious how to efficiently use this information. Here, to address both of these issues, we, first, identify optimal community structure of given networks in terms of modularity by utilizing a recently introduced community detection method. Second, we develop an approach to use this community information to extract hidden information from a network. When applied to a protein-protein interaction network, the proposed method outperforms current state-of-the-art methods that use only the local information of a network. The method is generally applicable to networks from many areas.