GraphMaps: Browsing Large Graphs as Interactive Maps

  • Lev NachmansonEmail author
  • Roman Prutkin
  • Bongshin Lee
  • Nathalie Henry Riche
  • Alexander E. Holroyd
  • Xiaoji Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9411)


Algorithms for laying out large graphs have seen significant progress in the past decade. However, browsing large graphs remains a challenge. Rendering thousands of graphical elements at once often results in a cluttered image, and navigating these elements naively can cause disorientation. To address this challenge we propose a method called GraphMaps, mimicking the browsing experience of online geographic maps.

GraphMaps creates a sequence of layers, where each layer refines the previous one. During graph browsing, GraphMaps chooses the layer corresponding to the zoom level, and renders only those entities of the layer that intersect the current viewport. The result is that, regardless of the graph size, the number of entities rendered at each view does not exceed a predefined threshold, yet all graph elements can be explored by the standard zoom and pan operations.

GraphMaps preprocesses a graph in such a way that during browsing, the geometry of the entities is stable, and the viewer is responsive. Our case studies indicate that GraphMaps is useful in gaining an overview of a large graph, and also in exploring a graph on a finer level of detail.


Large Graph Candidate Node Group Vertex Edge Route Layout Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We are grateful to Roberto Sonnino for the useful discussions on the rendering of the tile images in a background thread, and to Itzhak Benenson for sharing with us his ideas on the visualization style.


  1. 1.
    Abello, J., van Ham, F., Krishnan, N.: Ask-graphview: a large scale graph visualization system. IEEE Trans. Vis. Comput. Graph. 12(5), 669–676 (2006)CrossRefGoogle Scholar
  2. 2.
    Abello, J., Kobourov, S.G., Yusufov, R.: Visualizing large graphs with compound-fisheye views and treemaps. In: Pach, J. (ed.) GD 2004. LNCS, vol. 3383, pp. 431–441. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  3. 3.
    Auber, D.: Using Strahler numbers for real time visual exploration of huge graphs. In: Computer Vision and Graphics (ICCVG’02), pp. 56–69 (2002)Google Scholar
  4. 4.
    Auber, D.: Tulip - a huge graph visualization framework. In: Graph Drawing Software, pp. 105–126 (2004)Google Scholar
  5. 5.
    Auber, D., Chiricota, Y., Jourdan, F., Melançon, G.: Multiscale visualization of small world networks. In: IEEE Symposium on Information Visualization (INFOVIS’03), pp. 75–81 (2003)Google Scholar
  6. 6.
    Balzer, M., Deussen, O.: Level-of-detail visualization of clustered graph layouts. In: Asia-Pacific Symposium on Information Visualisation (APVIS’07), pp. 133–140. IEEE (2007)Google Scholar
  7. 7.
    Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks. In: International AAAI Conference on Weblogs and Social Media (ICWSM’09) (2009)Google Scholar
  8. 8.
    Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Brandes, U., Pich, C.: Eigensolver methods for progressive multidimensional scaling of large data. In: Kaufmann, M., Wagner, D. (eds.) GD 2006. LNCS, vol. 4372, pp. 42–53. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  10. 10.
    Brunel, E., Gemsa, A., Krug, M., Rutter, I., Wagner, D.: Generalizing geometric graphs. In: Speckmann, B. (ed.) GD 2011. LNCS, vol. 7034, pp. 179–190. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  11. 11.
    Eades, P., Feng, Q.-W.: Multilevel visualization of clustered graphs. In: North, S.C. (ed.) GD 1996. LNCS, vol. 1190, pp. 101–112. Springer, Heidelberg (1997) CrossRefGoogle Scholar
  12. 12.
    van den Elzen, S., van Wijk, J.: Multivariate network exploration and presentation: from detail to overview via selections and aggregations. IEEE Trans. Vis. Comput. Graph. 20(12), 2310–2319 (2014)CrossRefGoogle Scholar
  13. 13.
    Gansner, E.R., Koren, Y., North, S.C.: Topological fisheye views for visualizing large graphs. IEEE Trans. Vis. Comput. Graph. 11(4), 457–468 (2005)CrossRefGoogle Scholar
  14. 14.
    Gansner, E., Hu, Y., Kobourov, S.: Gmap: visualizing graphs and clusters as maps. In: IEEE Pacific Visualization Symposium (PacificVis’10), pp. 201–208. IEEE (2010)Google Scholar
  15. 15.
    van Ham, F., van Wijk, J.: Interactive visualization of small world graphs. In: IEEE Symposium on Information Visualization (INFOVIS’04), pp. 199–206 (2004)Google Scholar
  16. 16.
    van Ham, F., Perer, A.: Search, show context, expand on demand: supporting large graph exploration with degree-of-interest. IEEE Trans. Vis. Comput. Graph. 15(6), 953–960 (2009)CrossRefGoogle Scholar
  17. 17.
    Henry, N., Bezerianos, A., Fekete, J.D.: Improving the readability of clustered social networks using node duplication. IEEE Trans. Vis. Comput. Graph. 14(6), 1317–1324 (2008)CrossRefGoogle Scholar
  18. 18.
    Henry, N., Fekete, J.D., McGuffin, M.J.: NodeTrix: a hybrid visualization of social networks. IEEE Trans. Vis. Comput. Graph. 13(6), 1302–1309 (2007)CrossRefGoogle Scholar
  19. 19.
    Nachmanson, L., Prutkin, R., Lee, B., Riche, N.H., Holroyd, A.E., Chen, X.: Graphmaps: Browsing large graphs as interactive maps. CoRR arXiv:1506.06745 (2015)
  20. 20.
    Nocaj, A., Ortmann, M., Brandes, U.: Untangling hairballs. In: Duncan, C., Symvonis, A. (eds.) GD 2014. LNCS, vol. 8871, pp. 101–112. Springer, Heidelberg (2014) Google Scholar
  21. 21.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Technical report 1999–66, Stanford InfoLab (1999)Google Scholar
  22. 22.
    Perer, A., Shneiderman, B.: Balancing systematic and flexible exploration of social networks. IEEE Trans. Vis. Comput. Graph. 12(5), 693–700 (2006)CrossRefGoogle Scholar
  23. 23.
    Shewchuk, J.R.: Delaunay refinement algorithms for triangular mesh generation. Comput. Geom. Theory Appl. 22(1–3), 21–74 (2002)zbMATHMathSciNetCrossRefGoogle Scholar
  24. 24.
    Traud, A.L., Kelsic, E.D., Mucha, P.J., Porter, M.A.: Comparing community structure to characteristics in online collegiate social networks. SIAM Rev. 53(3), 526–543 (2011)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Wu, H.-Y., Takahashi, S., Lin, C.-C., Yen, H.-C.: A zone-based approach for placing annotation labels on metro maps. In: Dickmann, L., Volkmann, G., Malaka, R., Boll, S., Krüger, A., Olivier, P. (eds.) SG 2011. LNCS, vol. 6815, pp. 91–102. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  26. 26.
    Zinsmaier, M., Brandes, U., Deussen, O., Strobelt, H.: Interactive level-of-detail rendering of large graphs. IEEE Trans. Vis. Comput. Graph. 18(12), 2486–2495 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lev Nachmanson
    • 1
    Email author
  • Roman Prutkin
    • 2
  • Bongshin Lee
    • 1
  • Nathalie Henry Riche
    • 1
  • Alexander E. Holroyd
    • 1
  • Xiaoji Chen
    • 3
  1. 1.Microsoft ResearchRedmondUSA
  2. 2.Karlsruhe Institute of TechnologyKarlsruheGermany
  3. 3.MicrosoftRedmondUSA

Personalised recommendations