A Graph Layout Algorithm for Scale-free Network

척도 없는 네트워크를 위한 그래프 레이아웃 알고리즘

  • 조용만 (강릉대학교 컴퓨터공학과) ;
  • 강태원 (강릉대학교 컴퓨터공학과)
  • Published : 2007.06.15

Abstract

A network is an important model widely used in natural and social science as well as engineering. To analyze these networks easily it is necessary that we should layout the features of networks visually. These Graph-Layout researches have been performed recently according to the development of the computer technology. Among them, the Scale-free Network that stands out in these days is widely used in analyzing and understanding the complicated situations in various fields. The Scale-free Network is featured in two points. The first, the number of link(Degree) shows the Power-function distribution. The second, the network has the hub that has multiple links. Consequently, it is important for us to represent the hub visually in Scale-free Network but the existing Graph-layout algorithms only represent clusters for the present. Therefor in this thesis we suggest Graph-layout algorithm that effectively presents the Scale-free network. The Hubity(hub+ity) repulsive force between hubs in suggested algorithm in this thesis is in inverse proportion to the distance, and if the degree of hubs increases in a times the Hubity repulsive force between hubs is ${\alpha}^{\gamma}$ times (${\gamma}$??is a connection line index). Also, if the algorithm has the counter that controls the force in proportion to the total node number and the total link number, The Hubity repulsive force is independent of the scale of a network. The proposed algorithm is compared with Graph-layout algorithm through an experiment. The experimental process is as follows: First of all, make out the hub that exists in the network or not. Check out the connection line index to recognize the existence of hub, and then if the value of connection line index is between 2 and 3, then conclude the Scale-free network that has a hub. And then use the suggested algorithm. In result, We validated that the proposed Graph-layout algorithm showed the Scale-free network more effectively than the existing cluster-centered algorithms[Noack, etc.].

네트워크는 공학이나 자연과학은 물론이고 사회과학의 여러 분야를 연구하는데 중요하게 사용되는 모델이다. 이런 네트워크를 좀 더 쉽게 분석하기 위해서는 시각적으로 네트워크의 특징을 잘 나타내는 것이 필요하다. 이러한 그래프 레이아웃 연구는 컴퓨터 기술이 발달함에 따라 많이 연구되고 있다. 그 중에서 요즘 새롭게 부각되고 있는 척도 없는(Scale-free) 네트워크는 다양한 분야에서 복잡한 현상들을 분석하고 이해하는데 유용하게 쓰이고 있다. 이 네트워크의 특징은 링크의 수(Degree)가 멱함수(power law) 분포를 보이고, 다수의 링크를 가지는 허브가 존재함이 알려졌다. 따라서 척도 없는 네트워크에서는 허브를 시각적으로 잘 표현하는 것이 중요하지만 기존의 그래프 레이아웃 알고리즘은 클러스터를 잘 표현하는 정도이다. 그래서 본 논문에서는 척도 없는 네트워크를 잘 표현하는 그래프 레이아웃 알고리즘을 제안한다. 본 논문에서 제안한 알고리즘에서 허브들 간에 작용하는 허브성 척력이 거리에 반비례하고, 허브들의 degree가 a배 증가하면, 허브사이에 작용하는 척력의 크기는 ${\alpha}^{\gamma}({\gamma}$는 연결선 지수)배가 된다. 또한, 전체 노드수와 전체 링크수에 따라 적용되는 힘의 크기를 조정하는 계수를 두어서 네트워크의 규모에 관계없이 허브성 척력이 적용되는 특성이 있다. 제안한 알고리즘이 허브를 잘 표현하는 그래프 레이아웃 알고리즘인지를 기존의 방식과 실험을 통해서 비교하였다. 실험의 절차는 먼저 네트워크에 허브가 존재하는지를 식별한다. 허브의 존재를 식별하기 위한 방법은 연결선 지수를 확인하고, 연결선 지수의 값이 2와 3사이에 있으면 허브가 존재하는 척도 없는 네트워크로 판단한다. 다음은 이 네트워크의 레이아웃 작성에 제안한 알고리즘을 사용한다. 그 결과, 제안한 그래프 레이아웃 알고리즘이 기존의 Noack등의 클러스터중심의 알고리즘에 비해서 척도 없는 네트워크의 허브를 확실히 잘 보여주고 있음을 확인할 수 있었다.

Keywords

References

  1. Gray William Flake, The Computational Beauty of Nature, A Bradford Book, the MIT Press, 1998
  2. S. Wasserman and K. Faust, Social networks analysis, Cambridge University Press, Cambridge (1994)
  3. D. Watts and S. H. Strogatz, 'Collective dynamics of small world networks,' Nature(London) 393, 440, 1998 https://doi.org/10.1038/30918
  4. B.A. Huberman and L.A. Adamic, 'Growth dynamics of the world-wide web,' Nature 401, 131, 1999 https://doi.org/10.1038/43604
  5. R. Albert, H. Jeong, and A.-L. Barabasi, 'Diameter of the world-wide web,' Nature 401, 130-131, 1999 https://doi.org/10.1038/43601
  6. Charles J. Alpert and Andrew B. Kahng, 'Recent directions in netlist partitioning: A survey,' Technical report, Computer Science Department, University of California at Los Angeles, 1995
  7. M. Kaufmann, D. Wagner (Eds.), Drawing Graphs: Methods and Models, Lecture Notes in Computer Science. Springer Berlin / Heidelberg. Volume 2025, 2001
  8. Ivan Herman, Guy Melanc¸on, and M. Scott Marshall, Graph visualization and navigation in information visualization. volume 6(1), pages 24?43, 2000 https://doi.org/10.1109/2945.841119
  9. Josep diaz, Jordi Petit and Maria Serna, 'A Survey of Graph Layout Problems,' ACM Computins Surveys, Vol.34, No.3, pp.313-356 September 2002 https://doi.org/10.1145/568522.568523
  10. I. Herman, G. Melancon, MS Marshall. 'Graph Visualization and Navigation in Information Visualization : a Survey,' In: IEEE Transactions on Visualization and Computer Graphics, 6(1), pp. 24-43, 2000 https://doi.org/10.1109/2945.841119
  11. I. F. Cruz and J. P. Twarog, '3D Graph Drawing with Simulated Annealing', Proceedings of the Symposium on Graph Drawing GD '95, Springer-Verlag, pp.162-165, 1995
  12. R. Davidson and D. Harel, 'Drawing Graphs Nicely Using Simulated Annealing,' ACM Transaction on Graphics, Vol.15, No.4, pp.301-331, 1996 https://doi.org/10.1145/234535.234538
  13. P. Eades, 'A Heuristic for Graph Drawing,' Congressus Numerantium, Vol.42, pp.149-160, 1984
  14. C.-S Jeong and A. Pang, 'Reconfigurable Disc Trees for Visualizing Large Hierarchical Information Space,' Proceedings of the IEEE Symposium on Information Visualization (InfoViz'98), IEEE CS Press, 1998 https://doi.org/10.1109/INFVIS.1998.729555
  15. G. di Battista, P. Eades, R. Tamassia, and I.G. Tollis, 'Algorithms for drawing graphs: an annotated bibliography,' Computational Geometry: Theory and Applications, Vol.4, No.5, pp.235-282, 1994 https://doi.org/10.1016/0925-7721(94)00014-X
  16. Andre M.S. Barreto, Helio J.C. Barbosa, 'Graph Layout Using a Genetic Algorithm,' sbrn, p. 179, VI Brazilian Symposium on Neural Networks (SBRN'00), 2000 https://doi.org/10.1109/SBRN.2000.889735
  17. J. Kleinberg, 'The small-world phenomenon: An algorithmic perspective,' In Proc. 32nd ACM Symposium on Theory of Computing, 2000 https://doi.org/10.1145/335305.335325
  18. R. Albert and A.-L. Barabasi, 'Statistical mechanics of complex networks,' Rev. Mod. Phys. 74, 4797, 2002 https://doi.org/10.1103/RevModPhys.74.47
  19. M.E.J. Newman, 'The structure and function of complex networks,' SIAM Review 45, 167256, 2003 https://doi.org/10.1137/S003614450342480
  20. A.-L. Barabasi and R. Albert. Emergence of scaling in random networks. Science, 286: 509-512, 1999 https://doi.org/10.1126/science.286.5439.509
  21. P. Eades and Q.-W. Feng, 'Multilevel Visualization of Clustered Graphs,' Proceedings of the Symposium on Graph Drawing GD '96, Springer-Verlag, pp.101-112, 1997
  22. A. Noack. 'An energy model for visual graph clustering,' In Proc. 11th Int. Symp. on Graph Drawing, pages 425?436. Springer-Verlag, 2003
  23. M. Faloutsos, P. Faloutsos, and C. Faloutsos, On power-law relationships of the internet topology, Computer Communications Review 29, 251 (1999) https://doi.org/10.1145/316194.316229
  24. Mark, Buchanan, 'Ubiquity: the science of history... or why the world is simpler than we think,' 2001
  25. D. Watts. 'Small world: the dynamics of networks between order and randomness,' Princeton, 1999
  26. M. E. J. Newman, 'Power laws, Pareto distributions and Zipf's law,' Contemporary Physics 46, 323-351, 2005 https://doi.org/10.1080/00107510500052444
  27. Mark E. J. Newman, Stevens H. Strogatz, and Duncan J. Watts. 'Random graphs with arbitrary degree distributions and their applications,' Physics Reviews E, 64, 2001 https://doi.org/10.1103/PhysRevE.64.026118
  28. Bollobas B, Riordan O, Spencer J, Tusnady G, 'The Degree Sequence of a Scale-Free Random Graph Process,' Random Structures and Algori thms 18, 279-290, May 2001 https://doi.org/10.1002/rsa.1009
  29. A. Noack. 'Energy models for drawing clustered small-world graphs,' Technical Report 07/03, Institute of Computer Science, Brandenburg University of Technology at Cottbus, 2003
  30. Ulrik Brandes. Drawing on physical analogies. In M. Kaufmann and D. Wagner, editors, Drawing Graphs, LNCS 2025, pages 71-86. Springer-Verlag, Berlin, 2001
  31. Thomas M. J. Fruchterman, Edward M. Reingold, 'Graph Drawing by Force-directed Placement,' Software-Practice and Experience archive Volume 21(11), November 1991 https://doi.org/10.1002/spe.4380211102
  32. Bing Wang, Zhongzhi Zhang, Huanwen Tang, Zhilong Xiu. 'Evolving Scale-Free Network Model with Tunable Clustering,' International Journal of Modern Physics B November 16, 2005 https://doi.org/10.1142/S0217979205032437