Comparing Link Filtering Backbone Techniques in Real-World Networks

Filtering Real World Networks: A Correlation Analysis of Statistical Backbone Techniques

Networks are an invaluable tool for representing and understanding complex systems. They offer a wide range of applications, including identifying crucial nodes, uncovering communities, and exploring network formation. However, when dealing with large networks, the computational challenge can be overwhelming. Fortunately, researchers have developed several techniques to address this issue by reducing network size while preserving its fundamental properties [1-9]. To achieve this goal, two main approaches have emerged: structural and statistical methods. Structural methods aim to keep a set of topological features of the network while reducing its size. In contrast, statistical methods elimi…

An empirical investigation of Backbone Filtering Techniques in weighted Complex Networks

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, an…

NetBone: A Python Package for Extracting Backbones of Weighted Networks

NetBone is a new open-source Python package designed to simplify analyzing complex networks. With a wide range of techniques available, Net-Bone allows researchers to extract the backbone of a network while preserving its essential structure. The package includes nine structural methods and five statistical techniques, offering users a comprehensive solution to network analysis. It is user-friendly and straightforward to use, with easy installation. The package accepts different types of inputs, including data frames or Networkx graphs, and provides evaluation measures for comparative purposes. Additionally, NetBone offers an option to generate plots. Its versatility makes it a valuable too…

A Comparison of Model-Based Backbone Filtering Techniques in the Air Transportation Network