TY - JOUR A1 - Cecchini, Gloria A1 - Schelter, Björn T1 - Analytical approach to network inference BT - Investigating degree distribution JF - Physical review : E, Statistical, nonlinear and soft matter physics N2 - When the network is reconstructed, two types of errors can occur: false positive and false negative errors about the presence or absence of links. In this paper, the influence of these two errors on the vertex degree distribution is analytically analyzed. Moreover, an analytic formula of the density of the biased vertex degree distribution is found. In the inverse problem, we find a reliable procedure to reconstruct analytically the density of the vertex degree distribution of any network based on the inferred network and estimates for the false positive and false negative errors based on, e.g., simulation studies. Y1 - 2018 U6 - https://doi.org/10.1103/PhysRevE.98.022311 SN - 2470-0045 SN - 2470-0053 VL - 98 IS - 2 PB - American Physical Society CY - College Park ER - TY - JOUR A1 - Cecchini, Gloria A1 - Thiel, Marco A1 - Schelter, Björn A1 - Sommerlade, Linda T1 - Improving network inference BT - the impact of false positive and false negative conclusions about the presence or absence of links JF - Journal of neuroscience methods N2 - Background: A reliable inference of networks from data is of key interest in the Neurosciences. Several methods have been suggested in the literature to reliably determine links in a network. To decide about the presence of links, these techniques rely on statistical inference, typically controlling the number of false positives, paying little attention to false negatives. New method: In this paper, by means of a comprehensive simulation study, we analyse the influence of false positive and false negative conclusions about the presence or absence of links in a network on the network topology. We show that different values to balance false positive and false negative conclusions about links should be used in order to reliably estimate network characteristics. We propose to run careful simulation studies prior to making potentially erroneous conclusion about the network topology. Results: Our analysis shows that optimal values to balance false positive and false negative conclusions about links depend on the network topology and characteristic of interest. Comparison with existing methods: Existing methods rely on a choice of the rate for false positive conclusions. They aim to be sure about individual links rather than the entire network. The rate of false negative conclusions is typically not investigated. Conclusions: Our investigation shows that the balance of false positive and false negative conclusions about links in a network has to be tuned for any network topology that is to be estimated. Moreover, within the same network topology, the results are qualitatively the same for each network characteristic, but the actual values leading to reliable estimates of the characteristics are different. KW - Network inference KW - Node degree distribution KW - False positive KW - False negative KW - Statistical inference Y1 - 2018 U6 - https://doi.org/10.1016/j.jneumeth.2018.06.011 SN - 0165-0270 SN - 1872-678X VL - 307 SP - 31 EP - 36 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schelter, Björn A1 - Winterhalder, Matthias A1 - Dahlhaus, Rainer A1 - Kurths, Jürgen A1 - Timmer, Jens T1 - Partial phase synchronization for multivariate synchronizing systems N2 - Graphical models applying partial coherence to multivariate time series are a powerful tool to distinguish direct and indirect interdependencies in multivariate linear systems. We carry over the concept of graphical models and partialization analysis to phase signals of nonlinear synchronizing systems. This procedure leads to the partial phase synchronization index which generalizes a bivariate phase synchronization index to the multivariate case and reveals the coupling structure in multivariate synchronizing systems by differentiating direct and indirect interactions. This ensures that no false positive conclusions are drawn concerning the interaction structure in multivariate synchronizing systems. By application to the paradigmatic model of a coupled chaotic Roessler system, the power of the partial phase synchronization index is demonstrated Y1 - 2006 UR - http://prl.aps.org/abstract/PRL/v96/i20/e208103 U6 - https://doi.org/10.1103/Physrevlett.96.208103 ER -