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Improving Circular Layout Algorithm for Social Network Visualization Using Genetic Algorithm

  • Babak TeimourpourEmail author
  • Bahram Asgharpour
Chapter
Part of the Lecture Notes in Social Networks book series (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.

Keywords

Visualization Layout Genetic algorithm Minimum crossing number Social network analysis 

References

  1. 1.
    Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (1sted.). Cambridge, New York: Cambridge University Press.CrossRefGoogle Scholar
  2. 2.
    Chonbodeechalermroong, A., & Hewett, R. (2017). Towards visualizing big data with large-scale edge constraint graph drawing. Big Data Research, 10(Supplement C), 21–32.CrossRefGoogle Scholar
  3. 3.
    Herman, I., Melançon, G., & Marshall, M. S. (2000). Graph visualization and navigation in information visualization: A survey. IEEE Transactions on Visualization and Computer Graphics, 6(1), 24–43.CrossRefGoogle Scholar
  4. 4.
    Garey, M., & Johnson, D. (1983). Crossing number is NP-complete. SIAM Journal on Algebraic Discrete Methods, 4(3), 312–316.CrossRefGoogle Scholar
  5. 5.
    Masuda, S., Kashiwabara, T., Nakajima, K., & Fujisawa, K. On the NP-completeness of a computer network layout problem [Online]. Retrieved October 13, 2017, from https://www.researchgate.net/publication/247043450_On_the_NP-completeness_of_a_computer_ network_layout_problem.
  6. 6.
    Bachmaier, C., Buchner, H., Forster, M., & Hong, S.-H. (2010). Crossing minimization in extended level drawings of graphs. Discrete Applied Mathematics, 158(3), 159–179.CrossRefGoogle Scholar
  7. 7.
    Brandenburg, F. J. (1997). Graph clustering I: Cycles of cliques. In G. DiBattista (Ed.), Graph drawing. GD 1997. Lecture notes in computer science (Vol. 1353). Berlin: Springer.CrossRefGoogle Scholar
  8. 8.
    Doğrusöz, U., Madden, B., & Madden, P. (1997). Circular layout in the Graph Layout toolkit. In S. North (Ed.), Graph drawing. GD 1996. Lecture notes in computer science (Vol. 1190). Berlin: Springer.CrossRefGoogle Scholar
  9. 9.
    Six, J. M., & Tollis, I. G. (2006). A framework and algorithms for circular drawings of graphs. Journal of Discrete Algorithms, 4(1), 25–50.CrossRefGoogle Scholar
  10. 10.
    Six, J. M., & Tollis, I. G. (1999). Circular drawings of biconnected graphs. In Selected papers from the International Workshop on Algorithm Engineering and Experimentation (pp. 57–73). London, UK.Google Scholar
  11. 11.
    Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695, 1–9.Google Scholar
  12. 12.
    Tamassia, R. (Ed.). (2013). Handbook of graph drawing and visualization (1st ed.). New York: Chapman and Hall/CRC.Google Scholar
  13. 13.
    Kokosiński, Z., Kołodziej, M., & Kwarciany, M. (2004). Parallel genetic algorithm for graph coloring problem. In International Conference on Computational Science—ICCS 2004. LNCS 3036 (pp. 215–222).Google Scholar
  14. 14.
    Lipowski, A., & Lipowska, D. (2012). Roulette-wheel selection via stochastic acceptance. Physica A: Statistical Mechanics and its Applications, 391(6), 2193–2196.CrossRefGoogle Scholar
  15. 15.
    Mitchell, M. (1998). An introduction to genetic algorithms. Cambridge, MA: MIT Press.Google Scholar
  16. 16.
    Ronco, C. C. D., & Benini, E. (2014). A simplex-crossover-based multi-objective evolutionary algorithm. In IAENG Transactions on Engineering Technologies (pp. 583–598). The Netherlands: SpringerGoogle Scholar
  17. 17.
    Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms (3rd ed.). Cambridge, MA: The MIT Press.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of IT Engineering, School of Industrial and Systems EngineeringTarbiat Modares UniversityTehranIran

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