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The Analysis of the Structural Features of Complex Networks According to Their Types

Ghorbani, Nazila | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 45106 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Habibi, Jafar; Hemmatyar, Mohammad Afshin
  7. Abstract:
  8. Nowadays, the world is based on the interaction between individuals, groups and different systems. The actual networks that have a complex structure and behavior are called complex networks. Complex networks are one of the new knowledge that studies the connections. The complex systems represented as graph, with non-trivial topological features—features that do not occur in simple networks.With the vast development of computer networks, complex networks appear in different categories such as social networks, citation networks, collaboration networks and communication networks. Data mining is the process of exploring hidden knowledge in data bases and it has applications in complex networks. Data mining aims to discover patterns and extract useful information from a large amount of data. Nowadays, data mining applications have spread in a large number of fields such as marketing, fraud detections, Knowledge Discovery and Data mining (KDD), etc. In recent years, structural properties of complex networks and graph classification are studied.Classification is one of the most common techniques in data mining. Graph classification become a significance step in numerous applications, such as security, the web, social networks analysis.In this thesis, first a set of real networks was gathered. Second, the structural features such as modularity, density, clustering coefficient of each network instance was extracted. The result is a dataset of structural features in which each record consists of different features in a complex networks.After this part, a method was proposed for networks classification. For this reason, the different classification methods of complex networks were tested for the selecting of the appropriate classification method with a higher accuracy
  9. Keywords:
  10. Data Mining ; Classification ; Accuracy ; Complex Network ; Structural Features

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