Many real-world networks' size and density hinder visualization and graph processing. Several approaches have been developed over the years to reduce the network size while representing the original network as well as possible. "Edge-filtering" techniques focus on removing nodes and edges among the so-called backbone extraction techniques. They can be classified further into "structural" and "statistical". The structural techniques, such as the High-Salience-Skeleton, Doubly-Stochastic Transformation, and the Distance Backbone filter edges according to a criterion allowing the latent structure of the network to emerge. Statistical techniques such as the Disparity Filter, Noise Corrected, and Pólya Filter assess the significance of an edge according to a predefined null model and eliminate the least significant advantages. In this study, we perform an extensive comparative investigation of the forenamed influential filtering techniques using two performance criteria.