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Automatic Discovery of Families of Network Generative Processes

  • Telmo MenezesEmail author
  • Camille Roth
Conference paper
First Online:
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Designing plausible network models typically requires scholars to form a priori intuitions on the key drivers of network formation. Oftentimes, these intuitions are supported by the statistical estimation of a selection of network evolution processes which will form the basis of the model to be developed. Machine learning techniques have lately been introduced to assist the automatic discovery of generative models. These approaches may more broadly be described as “symbolic regression,” where fundamental network dynamic functions, rather than just parameters, are evolved through genetic programming. This chapter first aims at reviewing the principles, efforts, and the emerging literature in this direction, which is very much aligned with the idea of creating artificial scientists. Our contribution then aims more specifically at building upon an approach recently developed by us (Menezes and Roth, Sci Rep 4:6284, 2014) in order to demonstrate the existence of families of networks that may be described by similar generative processes. In other words, symbolic regression may be used to group networks according to their inferred genotype (in terms of generative processes) rather than their observed phenotype (in terms of statistical/topological features). Our empirical case is based on an original dataset of 238 anonymized ego-centered networks of Facebook friends, further yielding insights on the formation of sociability networks.

Keywords

Network generators Social networks Machine learning Genetic programming Network families Symbolic regression 
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Notes

Acknowledgements

We are grateful to the members of our “Algopol” project team (ANR-12-CORD-0018) who organized most of the Facebook survey, including Irène Bastard, Dominique Cardon, Raphaël Charbey, Guilhem Fouetillou, Christophe Prieur, and Stéphane Raux. We further acknowledge interesting discussions with Jean-Philippe Cointet and David Fourquet regarding generative families, as well as the constructive feedback of our anonymous reviewers. This paper has been partially supported by the “Algodiv” grant (ANR-15-CE38-0001) funded by the ANR (French National Agency of Research).

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Authors and Affiliations

  1. 1.Centre Marc Bloch (UMIFRE CNRS/MAE)Computational Social Science TeamBerlinGermany
  2. 2.CNRSParisFrance

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