A comparative analysis of approaches to network-dismantling

Abstract

Estimating, understanding, and improving the robustness of networks has many application areas such as bioinformatics, transportation, or computational linguistics. Accordingly, with the rise of network science for modeling complex systems, many methods for robustness estimation and network dismantling have been developed and applied to real-world problems. The state-of-the-art in this field is quite fuzzy, as results are published in various domain-specific venues and using different datasets. In this study, we report, to the best of our knowledge, on the analysis of the largest benchmark regarding network dismantling. We reimplemented and compared 13 competitors on 12 types of random networks, including ER, BA, and WS, with different network generation parameters. We find that network metrics, proposed more than 20 years ago, are often non-dominating competitors, while many recently proposed techniques perform well only on specific network types. Besides the solution quality, we also investigate the execution time. Moreover, we analyze the similarity of competitors, as induced by their node rankings. We compare and validate our results on real-world networks. Our study is aimed to be a reference for selecting a network dismantling method for a given network, considering accuracy requirements and run time constraints.

Systematic Investigation of the Electronic Structure of Hematite Thin Films

“…When comparing valence band spectra of nominally hematite phases from the last decades quite drastic differences can be noticed . The features of the valence bands are different from one study to the other.…”

Scaling of percolation transitions on Erdös-Rényi networks under centrality-based attacks

“…Interestingly, the recalculated betweenness attack is particularly effective being comparable to the most effective methods to dismantle networks [15,23,24]. Also, the critical exponents of the percolation process associated with this attack are nontrivial, as they depart from the mean-field values observed in random attacks to ER networks and the other attacks evaluated in this work.…”

“…The most efficient algorithm so far known was proposed by Brandes, et al in [29] and runs like O(N M ), where N and M are the number of nodes and links in the network, respectively. The main reason for considering this measure is that it has been reported as the most efficient attack strategy for many networks, including both synthetic and real-world networks [14,15].…”

“…The effectiveness of the attack depends on the topological features of the network as well as on the type of attack. For this reason, several network architectures were studied under different attacks to evaluate both the robustness of networks and the effectiveness of attacks [13][14][15]. For instance, it is widely known that scale-free networks are fragile against centrality targeted attacks [13,[16][17][18] but robust against random failures [18].…”

Activation of α‐Fe2O3 for Photoelectrochemical Water Splitting Strongly Enhanced by Low Temperature Annealing in Low Oxygen Containing Ambient

“…Clearly, Fe 2p 3/2 peaks around 711 eV, Fe 2p 1/2 peaks around 724 eV, and satellite peaks of Fe 3+ around 719 eV are identified for both samples. These values have been reported as typical binding energies for Fe 2 O 3 . However, a satellite peak of Fe 2+ around 716 eV that is typically attributed to oxygen vacancies could not be found.…”