Recognizing Power-law Graphs by Machine Learning Algorithms using a Reduced Set of Structural Features

  • Alane Lima Federal University of Paraná
  • André Vignatti Federal University of Paraná
  • Murilo Silva Federal University of Parana

Resumo


The empirical study of large real world networks in the last 20 years showed that a variety of real-world graphs are power-law. There are evidence that optimization problems seem easier in these graphs; however, for a given graph, classifying it as power-law is a problem in itself. In this work, we propose using machine learning algorithms (KNN, SVM, Gradient Boosting and Random Forests) for this task. We suggest a graph representation based on [Canning et al. 2018], but using a much simplified set of structural properties of the graph. We compare the proposed representation with the one generated by the graph2vec framework. The experiments attained high accuracy, indicating that a reduced set of structural graph properties is enough for the presented problem.

Palavras-chave: Power-law graphs, Graph representation, Machine Learning algorithms

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Publicado
15/10/2019
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LIMA, Alane; VIGNATTI, André; SILVA, Murilo. Recognizing Power-law Graphs by Machine Learning Algorithms using a Reduced Set of Structural Features. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 611-621. DOI: https://doi.org/10.5753/eniac.2019.9319.