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Homophily in the synthetic networks

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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

E Homophily in the synthetic networks

Figure 8: (a) Value of homophily in the synthetic networks. Parameters of the networks: N = 1000,⟨k⟩ = 30. The black horizontal line indicates the random case, i.e., for α = 0 which based on Eq. 8 gives us H = 1/NL where NL is the number of labels. The results are averaged over 100 realizations. (b) Maximum angular distance between nodes in the same community in function of parameter α. The results are averaged over 100 realizations.


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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