Extraction of hidden information by efficient community detection in 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.

17 pages, 2 figures and 2 tables

References in corpus (7)

• Community detection in graphs
• Uncovering the overlapping community structure of complex networks in nature and society
• Benchmark graphs for testing community detection algorithms
• The performance of modularity maximization in practical contexts
• Quantifying and identifying the overlapping community structure in networks
• Deciphering Network Community Structure by Surprise
• Modularity measure of networks with overlapping communities

Cited by in corpus (2)

• Link community detection through global optimization and the inverse resolution limit of partition density
• Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion