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Fluctuations in the performance of machine learning models

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Abstract and 1. Introduction

  1. Related work

  2. HypNF Model

    3.1 HypNF Model

    3.2 The S1/H2 model

    3.3 Assigning labels to nodes

  3. HypNF benchmarking framework

  4. Experiments

    5.1 Parameter Space

    5.2 Machine learning models

  5. Results

  6. Conclusion, Acknowledgments and Disclosure of Funding, and References


A. Empirical validation of HypNF

B. Degree distribution and clustering control in HypNF

C. Hyperparameters of the machine learning models

D. Fluctuations in the performance of machine learning models

E. Homophily in the synthetic networks

F. Exploring the parameters’ space

D Fluctuations in the performance of machine learning models

Figure 7: Fluctuations of performance of the machine learning models. The difference of the standard deviation between high and low value of each parameter. We set NL = 6 and α = 10 for the NC task. The results are averaged over all other parameters.


Authors:

(1) Roya Aliakbarisani, this author contributed equally from Universitat de Barcelona & UBICS ([email protected]);

(2) Robert Jankowski, this author contributed equally from Universitat de Barcelona & UBICS ([email protected]);

(3) M. Ángeles Serrano, Universitat de Barcelona, UBICS & ICREA ([email protected]);

(4) Marián Boguñá, Universitat de Barcelona & UBICS ([email protected]).


This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.


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