Automatic Discovery of Families of Network Generative Processes

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Menezes:2017:DOOCN,

• title = "Automatic Discovery of Families of Network Generative Processes",
• author = "Telmo Menezes and Camille Roth",
• booktitle = "Dynamics On and Of Complex Networks III",
• booktitle2 = "Machine Learning and Statistical Physics Approaches",
• year = "2017",
• editor = "Fakhteh Ghanbarnejad and Rishiraj Saha Roy and Fariba Karimiand Jean-Charles Delvenne and Bivas Mitra",
• series = "Springer Proceedings in Complexity",
• pages = "83--111",
• address = "Indianapolis, USA",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, computational social sciences, network science, evolutionary computations, machine learning, ML, social network analysis, SNA, artificial intelligence, complex networks, computer science, neural and evolutionary computing, social and information networks, humanities and social sciences, methods and statistics, sociology",
• ISSN = "2213-8684",
• isbn13 = "978-3-030-14682-5",
• oai = "oai:HAL:hal-02165035v1",
• URL = "https://arxiv.org/abs/1906.12332",
• URL = "https://hal.archives-ouvertes.fr/hal-02165035",
• URL = "https://hal.archives-ouvertes.fr/hal-02165035/document",
• URL = "https://hal.archives-ouvertes.fr/hal-02165035/file/Automatic_Discovery_of_Families_of_Network_Generative_Processes__HAL_.pdf",
• DOI = "doi:10.1007/978-3-030-14683-2_4",
• 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, 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 data set of 238 anonymised ego-centered networks of Facebook friends, further yielding insights on the formation of sociability networks.",
• notes = "http://doocn.org/ SPCOM",
• annote = "Centre Marc Bloch (CMB) ; Ministere de l'Europe et des Affaires etrangeres (MEAE)-Bundesministerium fur Bildung und Forschung-Ministere de l'Education nationale, de l{'}Enseignement superieur et de la Recherche (M.E.N.E.S.R.)-Centre National de la Recherche Scientifique (CNRS)",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• contributor = "Centre Marc Bloch and This paper has been partially supported by the Algodiv grant (ANR-15-CE38-0001) funded by the ANR (French National Agency of Research). and Algopol ANR-12-CORD-0018,Politique des algorithmes(2012) and ALGODIV ANR-15-CE38-0001,Algodiv: Recommandation algorithmique et diversite des informations du web(2015)",
• description = "International audience",
• language = "en",
• relation = "info:eu-repo/semantics/altIdentifier/arxiv/1906.12332; info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-14683-2_4",
• rights = "info:eu-repo/semantics/OpenAccess",
• type = "info:eu-repo/semantics/bookPart",

}

Genetic Programming entries for Telmo Menezes Camille Roth

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