  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.
