Removing spurious interactions in complex networks

TL;DR

This paper addresses the challenge of removing false links in complex networks to improve data reliability, proposing a hybrid method that balances accuracy with preservation of network properties.

Contribution

It introduces a novel hybrid approach combining similarity indices and edge-betweenness centrality to effectively eliminate spurious links without disrupting network structure.

Findings

The hybrid method effectively removes spurious links.

It preserves network connectivity and functionalities.

Simple methods may distort network properties.

Abstract

Identifying and removing spurious links in complex networks is a meaningful problem for many real applications and is crucial for improving the reliability of network data, which in turn can lead to a better understanding of the highly interconnected nature of various social, biological and communication systems. In this work we study the features of different simple spurious link elimination methods, revealing that they may lead to the distortion of networks' structural and dynamical properties. Accordingly, we propose a hybrid method which combines similarity-based index and edge-betweenness centrality. We show that our method can effectively eliminate the spurious interactions while leaving the network connected and preserving the network's functionalities.