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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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2083-2567
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On Merging and Dividing Social Graphs

Pascal Held
  • Department of Knowledge Processing and Language Engineering Faculty of Computer Science, Otto von Guericke University Magdeburg Universitaetsplatz 2, 39106 Magdeburg, Germany
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/ Alexander Dockhorn
  • Department of Knowledge Processing and Language Engineering Faculty of Computer Science, Otto von Guericke University Magdeburg Universitaetsplatz 2, 39106 Magdeburg, Germany
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/ Rudolf Kruse
  • Department of Knowledge Processing and Language Engineering Faculty of Computer Science, Otto von Guericke University Magdeburg Universitaetsplatz 2, 39106 Magdeburg, Germany
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Published Online: 2015-04-30 | DOI: https://doi.org/10.1515/jaiscr-2015-0017

Abstract

Modeling social interaction can be based on graphs. However most models lack the flexibility of including larger changes over time. The Barabási-Albert-model is a generative model which already offers mechanisms for adding nodes. We will extent this by presenting four methods for merging and five for dividing graphs based on the Barabási- Albert-model. Our algorithms were motivated by different real world scenarios and focus on preserving graph properties derived from these scenarios. With little alterations in the parameter estimation those algorithms can be used for other graph models as well. All algorithms were tested in multiple experiments using graphs based on the Barabási- Albert-model, an extended version of the Barabási-Albert-model by Holme and Kim, the Watts-Strogatz-model and the Erdős-Rényi-model. Furthermore we concluded that our algorithms are able to preserve different properties of graphs independently from the used model. To support the choice of algorithm, we created a guideline which highlights advantages and disadvantages of discussed methods and their possible use-cases.

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About the article

Published Online: 2015-04-30

Published in Print: 2015-01-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2015-0017.

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© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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