Performs different approaches for network-based source estimation: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.


The main function for origin estimation of propagation processes on complex network is origin. Different methods are available: effective distance median ('edm'), recursive backtracking ('backtracking'), and centrality-based source estimation ('centrality'). For more details on the methodological background, we refer to the corresponding publications.


  • Manitz, J., J. Harbering, M. Schmidt, T. Kneib, and A. Schoebel (2017): Source Estimation for Propagation Processes on Complex Networks with an Application to Delays in Public Transportation Systems. Journal of Royal Statistical Society C (Applied Statistics), 66: 521-536.

  • Manitz, J., Kneib, T., Schlather, M., Helbing, D. and Brockmann, D. (2014) Origin detection during food-borne disease outbreaks - a case study of the 2011 EHEC/HUS outbreak in Germany. PLoS Currents Outbreaks, 1. <DOI: 10.1371/currents.outbreaks.f3fdeb08c5b9de7c09ed9cbcef5f01f2>

  • Comin, C. H. and da Fontoura Costa, L. (2011) Identifying the starting point of a spreading process in complex networks. Physical Review E, 84. <DOI: 10.1103/PhysRevE.84.056105>


Juliane Manitz with contributions by Jonas Harbering