Classification of Complex Networks Using Structural Analysis of Random Graph Models Rastsal Cizge Modellerinin Yapisal Analizi ile Karmasik Aglarin Siniflandirilmasi


28th Signal Processing and Communications Applications Conference, SIU 2020, Gaziantep, Turkey, 5 - 07 October 2020 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu49456.2020.9302344
  • City: Gaziantep
  • Country: Turkey
  • Keywords: Analysis of social networks, network classification, random graph model.
  • Hacettepe University Affiliated: Yes


© 2020 IEEE.Complex networks representing social interactions, brain activities, molecular structures have been studied widely nowadays to be able to understand and predict their characteristics as graphs. In this study, various real-world networks have been classified according to random graph models by making use of graph features. In the classification process, the most suitable machine learning algorithms are used and their performances are analyzed. Also, synthetic graphs generated by the random graph models are used in order to increase the success rate of the classification. Finally, the graph features are divided into different groups by using statistical tools to study their influence on the performance.