activity
20142021
most citedEstimating the sensitivity of centrality measures w.r.t. measurement errors

26 citations · 38 across the 5 of their papers we have counts for

collaborators

5 papers

physics.data-an2021★ 8 cited

Estimating covariant Lyapunov vectors from data

Christoph Martin, Nahal Sharafi, Sarah Hallerberg

Covariant Lyapunov vectors characterize the directions along which perturbations in dynamical systems grow. They have also been studied as predictors of critical transitions and ex…

cs.SI2020★ 1 cited

A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification

Christoph Martin, Meike Riebeling

Node embedding methods find latent lower-dimensional representations which are used as features in machine learning models. In the last few years, these methods have become extreme…

cs.SI2017

The impact of partially missing communities~on the reliability of centrality measures

Christoph Martin

Network data is usually not error-free, and the absence of some nodes is a very common type of measurement error. Studies have shown that the reliability of centrality measures is…

cs.SI2017★ 26 cited

Estimating the sensitivity of centrality measures w.r.t. measurement errors

Christoph Martin, Peter Niemeyer

Most network studies rely on an observed network that differs from the underlying network which is obfuscated by measurement errors. It is well known that such errors can have a se…

cs.SI2014★ 3 cited

Comparing the sensitivity of social networks, web graphs, and random graphs with respect to vertex removal

Christoph Martin, Peter Niemeyer

The sensitivity of networks regarding the removal of vertices has been studied extensively within the last 15 years. A common approach to measure this sensitivity is (i) removing s…