@article{HakeBodenbergerGroth2023,
  author    = {Hake, Tim and Bodenberger, Bernhard and Groth, Detlef},
  title     = {In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks},
  series = {Human biology and public health},
  volume    = {1},
  journal   = {Human biology and public health},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {2748-9957},
  doi       = {10.52905/hbph2023.1.63},
  pages     = {16},
  year      = {2023},
  abstract  = {The St. Nicolas House Algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant.},
  language  = {en}
}
@article{KralemannPikovskijRosenblum2014,
  author    = {Kralemann, Bjoern and Pikovskij, Arkadij and Rosenblum, Michael},
  title     = {Reconstructing effective phase connectivity of oscillator networks from observations},
  series = {New journal of physics : the open-access journal for physics},
  volume    = {16},
  journal   = {New journal of physics : the open-access journal for physics},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {1367-2630},
  doi       = {10.1088/1367-2630/16/8/085013},
  pages     = {21},
  year      = {2014},
  abstract  = {We present a novel approach for recovery of the directional connectivity of a small oscillator network by means of the phase dynamics reconstruction from multivariate time series data. The main idea is to use a triplet analysis instead of the traditional pairwise one. Our technique reveals an effective phase connectivity which is generally not equivalent to a structural one. We demonstrate that by comparing the coupling functions from all possible triplets of oscillators, we are able to achieve in the reconstruction a good separation between existing and non-existing connections, and thus reliably reproduce the network structure.},
  language  = {en}
}
@article{NovineMattssonGroth2022,
  author    = {Novine, Masiar and Mattsson, Cecilie Cordua and Groth, Detlef},
  title     = {Network reconstruction based on synthetic data generated by a Monte Carlo approach},
  series = {Human biology and public health},
  volume    = {2021},
  journal   = {Human biology and public health},
  number    = {3, Summer School Supplement},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {2748-9957},
  doi       = {10.52905/hbph2021.3.26},
  pages     = {23},
  year      = {2022},
  abstract  = {Background: Network models are useful tools for researchers to simplify and understand investigated systems. Yet, the assessment of methods for network construction is often uncertain. Random resampling simulations can aid to assess methods, provided synthetic data exists for reliable network construction. Objectives: We implemented a new Monte Carlo algorithm to create simulated data for network reconstruction, tested the influence of adjusted parameters and used simulations to select a method for network model estimation based on real-world data. We hypothesized, that reconstructs based on Monte Carlo data are scored at least as good compared to a benchmark. Methods: Simulated data was generated in R using the Monte Carlo algorithm of the mcgraph package. Benchmark data was created by the huge package. Networks were reconstructed using six estimator functions and scored by four classification metrics. For compatibility tests of mean score differences, Welch's t-test was used. Network model estimation based on real-world data was done by stepwise selection. Samples: Simulated data was generated based on 640 input graphs of various types and sizes. The real-world dataset consisted of 67 medieval skeletons of females and males from the region of Refshale (Lolland) and Nordby (Jutland) in Denmark. Results: Results after t-tests and determining confidence intervals (CI95\%) show, that evaluation scores for network reconstructs based on the mcgraph package were at least as good compared to the benchmark huge. The results even indicate slightly better scores on average for the mcgraph package. Conclusion: The results confirmed our objective and suggested that Monte Carlo data can keep up with the benchmark in the applied test framework. The algorithm offers the feature to use (weighted) un- and directed graphs and might be useful for assessing methods for network construction.},
  language  = {en}
}