Modularity-based Backbone Extraction in Weighted Complex Networks

Abstract: The constantly growing size of real-world networks is a great challenge. Therefore, building a compact version of networks allowing their analyses is a must. Backbone extraction techniques are among the leading solutions to reduce network size while preserving its features. Coarse-graining merges similar nodes to reduce the network size, while filter-based methods remove nodes or edges according to a specific statistical property. Since community structure is ubiquitous in real-world networks, preserving it in the backbone extraction process is of prime interest. To this end, we propose a filter-based method. The so-called "modularity vitality backbone" removes nodes with the lower contribution to the network's modularity. Experimental results show that the proposed strategy outperforms the "overlapping nodes ego backbone" and the "overlapping nodes and hub backbone." These two backbone extraction processes recently introduced have proved their efficacy to preserve better the information of the original network than the popular disparity filter.