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Improving Circular Layout Algorithm for Social Network Visualization Using Genetic Algorithm

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Book cover Applications of Data Management and Analysis

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Visualization is an important part of network analysis. It helps to find features of the network that are not easily identifiable. Graph data visualization tries to enable users to grasp difficult concepts or identify new patterns. One of the problems that prevents users from achieving this goal is the crossing number of a graph drawing. A crossing in graph data visualization is a point where two curves intersect. In the minimum crossing number problem, the goal is to find a drawing of G with minimum number of edge crossings. In particular, except for a few initial cases, the crossing number of graphs remains unknown. Circular layout is one of the graph drawing algorithms that is used for visualizing graph datasets. In this research we tried to minimize the number of edge crossings in a circular layout using genetic algorithm. The result is promising.

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Author information

Authors and Affiliations

  1. Department of IT Engineering, School of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

    Babak Teimourpour & Bahram Asgharpour

Authors
  1. Babak Teimourpour
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  2. Bahram Asgharpour
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Corresponding author

Correspondence to Babak Teimourpour .

Editor information

Editors and Affiliations

  1. Department Electrical & Computer Engineering, University of Calgary, Calgary, AB, Canada

    Mohammad Moshirpour

  2. Department Electrical & Computer Engineering, University of Calgary, Calgary, AB, Canada

    Behrouz H. Far

  3. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

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Teimourpour, B., Asgharpour, B. (2018). Improving Circular Layout Algorithm for Social Network Visualization Using Genetic Algorithm. In: Moshirpour, M., Far, B., Alhajj, R. (eds) Applications of Data Management and Analysis . Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-95810-1_3

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