An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks

TL;DR

Netbone, a Python package designed for assessing the performance of backbone extraction techniques in weighted networks, is introduced and it is shown how users can integrate a new backbone extraction method into the comparison framework.

Abstract

Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces netbone, a Python package designed for assessing the performance of backbone extraction techniques in weighted networks. Its comparison framework is the standout feature of netbone. Indeed, the tool incorporates state-of-the-art backbone extraction techniques. Furthermore, it provides a comprehensive suite of evaluation metrics allowing users to evaluate different backbones techniques. We illustrate the flexibility and effectiveness of netbone through the US air transportation network analysis. We compare the performance of different backbone extraction techniques using the evaluation metrics. We also show how users can integrate a new backbone extraction method into the comparison framework. netbone is publicly available as an open-source tool, ensuring its accessibility to researchers and practitioners. Promoting standardized evaluation practices contributes to the advancement of backbone extraction techniques and fosters reproducibility and comparability in research efforts. We anticipate that netbone will serve as a valuable resource for researchers and practitioners enabling them to make informed decisions when selecting backbone extraction techniques to gain insights into the structural and functional properties of complex systems.

Figures (10)

• Figure 1: The diagram illustrates the different modules provided in the netbone package and their interactions.
• Figure 2: The original Les Misérables network and its extracted backbones using the boolean_filter() with a default threshold of 0.8, threshold_filter() with a threshold value of 0.7, and fraction_filter() with a fraction of 0.15. N and E are the number of nodes and edges, respectively. The size of the nodes is proportional to the degree. The width of the links is proportional to the weights.
• Figure 3: A diagram illustrating the flow within netbone's comparison framework to compute the topological properties of the extracted backbone.
• Figure 4: A radar chart showing the topological properties of the extracted structural backbones plotted using netbone. The topological properties are the fraction of nodes, edges, and weights preserved in the backbone, density, average degree, and reachability of the extracted backbone.
• Figure 5: A diagram illustrating the flow within netbone comparison framework to compute the evolution of the topological properties as the threshold varies in the extracted backbone.