Centrality measures are essential tools for analyzing the structure and dynamics of graphs, such as knowledge or social networks.They reveal the significance and influence of individual nodes.However, their accuracy can be influenced by data quality, algorithms, and network properties.This study investigates errors in centrality measures within perpetuated networks.It focuses on network resilience and how these results may be used to develop efficient algorithms for centrality measures.It also investigates how perturbation strategies impact network resilience and predict connectivity in the perturbed network.By employing centrality measures (degree, betweenness, closeness, eigenvector), we identify critical nodes that significantly affect network connectivity and information flow.Additionally, statistical tests (Kolmogorov-Smirnov, Cramér-von Mises) assess network robustness and pinpoint critical transition points.This study, by outlining methods for error identification, quantification, and mitigation, offers valuable insights for enhancing network resilience across various domains, including infrastructure design and social network analysis.

The helical axis is a common tool used in biomechanical modeling to parameterize the motion of rigid objects. It encodes an object’s rotation around and translation along a unique axis. Visualizations of helical axes have helped to make kinematic data tangible. However, the analysis process often remains tedious, especially if complex motions are examined. We identify multiple key challenges: the absence of interactive tools for the computation and handling of helical axes, visual clutter in axis representations, and a lack of contextualization. We solve these issues by providing the first generalized framework for kinematic analysis with helical axes. Axis sets can be computed on-demand, interactively filtered, and explored in multiple coordinated views. We iteratively developed and evaluated the HAExplorer with active biomechanics researchers. Our results show that the techniques we introduce open up the possibility to analyze non-planar, compound, and interdependent motion data.