Robustness of Real-World Networks after Weight Thresholding with Strong Link Removal

Abstract

Weight thresholding (WT) is a method intended to decrease the number of links within weighted networks that may otherwise be excessively dense for network science applications. WT aims to remove links to simplify the network by holding most of the features of the original network. Here, we test the robustness and the efficacy of the node attack strategies on real-world networks subjected to WT that remove links of higher weight (strong links). We measure the network robustness along node removal with the largest connected component (LCC). We find that the real-world networks under study are generally robust when subjected to WT. Nonetheless, WT with strong link removal changes the efficacy of the attack strategies and the rank of node centralities. Also, WT with strong link removal may trigger a more significant change in the node centrality rank than WT by removing weak links. Network science research with the aim to find important/influential nodes in the network has to consider that simplifying the network with WT methodologies may change the node centrality.

Keywords

• complex networks
• network robustness
• weight thresholding
• link removal
• link pruning