Energy-Based Clustering of Graphs with Nonuniform Degrees (original) (raw)

References

1. Albert, R., Barabási, A.-L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74(1), 47–97 (2002)
   Article MathSciNet MATH Google Scholar
2. Alpert, C.J., Kahng, A.B.: Recent directions in netlist partitioning: A survey. Integration, the VLSI Journal 19(1-2), 1–81 (1995)
   Article MATH Google Scholar
3. Barnes, J., Hut, P.: A hierarchical O(N log N) force-calculation algorithm. Nature 324, 446–449 (1986)
   Article Google Scholar
4. Blythe, J., McGrath, C., Krackhardt, D.: The effect of graph layout on inference from social network data. In: Brandenburg, F.J. (ed.) GD 1995. LNCS, vol. 1027, pp. 40–51. Springer, Heidelberg (1996)
   Chapter Google Scholar
5. Davidson, R., Harel, D.: Drawing graphs nicely using simulated annealing. ACM Transactions on Graphics 15(4), 301–331 (1996)
   Article Google Scholar
6. Dengler, E., Cowan, W.: Human perception of laid-out graphs. In: Whitesides, S.H. (ed.) GD 1998. LNCS, vol. 1547, pp. 441–443. Springer, Heidelberg (1999)
   Chapter Google Scholar
7. Eades, P.: A heuristic for graph drawing. Congressus Numerantium 42, 149–160 (1984)
   MathSciNet Google Scholar
8. Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Software – Practice and Experience 21(11), 1129–1164 (1991)
   Article Google Scholar
9. Gajer, P., Goodrich, M.T., Kobourov, S.G.: A multi-dimensional approach to force-directed layouts of large graphs. In: Marks, J. (ed.) GD 2000. LNCS, vol. 1984, pp. 211–221. Springer, Heidelberg (2001)
   Chapter Google Scholar
10. Hachul, S., Jünger, M.: Drawing large graphs with a potential-field-based multilevel algorithm. In: Pach, J. (ed.) GD 2004. LNCS, vol. 3383, pp. 285–295. Springer, Heidelberg (2005)
    Chapter Google Scholar
11. Hall, K.M.: An r-dimensional quadratic placement algorithm. Management Science 17(3), 219–229 (1970)
    Article MATH Google Scholar
12. Harel, D., Koren, Y.: A fast multi-scale method for drawing large graphs. In: Marks, J. (ed.) GD 2000. LNCS, vol. 1984, pp. 183–196. Springer, Heidelberg (2001)
    Chapter Google Scholar
13. Kamada, T., Kawai, S.: An algorithm for drawing general undirected graphs. Information Processing Letters 31(1), 7–15 (1989)
    Article MATH MathSciNet Google Scholar
14. Kannan, R., Vempala, S., Vetta, A.: On clusterings: Good, bad and spectral. Journal of the ACM 51(3), 497–515 (2004)
    Article MathSciNet MATH Google Scholar
15. Leighton, T., Rao, S.: An approximate max-flow min-cut theorem for uniform multicommodity flow problems with applications to approximation algorithms. In: Proc. 29th Annual Symposium on Foundations of Computer Science (FOCS 1988), pp. 422–431. IEEE, Los Alamitos (1988)
    Google Scholar
16. Mancoridis, S., Mitchell, B.S., Rorres, C., Chen, Y., Gansner, E.R.: Using automatic clustering to produce high-level system organizations of source code. In: Proc. 6th IEEE International Workshop on Program Comprehension (IWPC 1998), pp. 45–52. IEEE, Los Alamitos (1998)
    Google Scholar
17. Noack, A.: An energy model for visual graph clustering. In: Liotta, G. (ed.) GD 2003. LNCS, vol. 2912, pp. 425–436. Springer, Heidelberg (2004)
    Chapter Google Scholar
18. Noack, A., Lewerentz, C.: A space of layout styles for hierarchical graph models of software systems. In: Proc. 2nd ACM Symposium on Software Visualization (SoftVis 2005), pp. 155–164. ACM, New York (2005)
    Chapter Google Scholar
19. Quigley, A.J., Eades, P.: FADE: Graph drawing, clustering, and visual abstraction. In: Marks, J. (ed.) GD 2000. LNCS, vol. 1984, pp. 197–210. Springer, Heidelberg (2001)
    Chapter Google Scholar
20. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
    Article Google Scholar
21. Walshaw, C.: A multilevel algorithm for force-directed graph drawing. In: Marks, J. (ed.) GD 2000. LNCS, vol. 1984, pp. 171–182. Springer, Heidelberg (2001)
    Chapter Google Scholar
22. Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Transaction on Pattern Analysis and Machine Intelligence 15(11), 1101–1113 (1993)
    Article Google Scholar

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