Journal of Visualization

, Volume 20, Issue 2, pp 205–215 | Cite as

Module-based visualization of large-scale graph network data

  • Chenhui Li
  • George BaciuEmail author
  • Yunzhe Wang
Regular Paper


The efficient visualization of dynamic network structures has become a dominant problem in many big data applications, such as large network analytics, traffic management, resource allocation graphs, logistics, social networks, and large document repositories. In this paper, we present a large-graph visualization system called ModuleGraph. ModuleGraph is a scalable representation of graph structures by treating a graph as a set of modules. The main objectives are: (1) to detect graph patterns in the visualization of large-graph data, and (2) to emphasize the interconnecting structures to detect potential interactions between local modules. Our first contribution is a hybrid modularity measure. This measure partitions the cohesion of the graph at various levels of details. We aggregate clusters of nodes and edges into several modules to reduce the overlap between graph components on a 2D display. Our second contribution is a k-clustering method that can flexibly detect the local patterns or substructures in modules. Patterns of modules are preserved by the ModuleGraph system to avoid information loss, while sub-graphs are clustered as a single node. Our experiments show that this method can efficiently support large-scale social and spatial network visualization.

Graphical Abstract

Graphical Abstract text


Network visualization Module grouping Graph drawing Information visualization Community detection 



The authors would like to acknowledge the partial support of the Hong Kong Research Grants Council Grants, GRF PolyU 5100/12E, IGRF PolyU 152142/15E, and Project 4-ZZFF from the Department of Computing, The Hong Kong Polytechnic University.


  1. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10008CrossRefGoogle Scholar
  2. Bostock M, Ogievetsky V, Heer J (2011) D3: data-driven documents. IEEE Trans Vis Comput Graph 17(12):2301–2309CrossRefGoogle Scholar
  3. Chae S, Majumder A, Gopi M (2012) HD-GraphViz: highly distributed graph visualization on tiled displays. In: Proceedings of the eighth indian conference on computer vision, graphics and image processing, ICVGIP ’12, pp 43:1–43:8Google Scholar
  4. Dorogovtsev SN, Goltsev AV, Mendes JFF (2006) \(k\)-core organization of complex networks. Phys Rev Lett 96:040601CrossRefzbMATHGoogle Scholar
  5. Dunne C, Shneiderman B (2013) Motif simplification: Improving network visualization readability with fan, connector, and clique glyphs. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’13. ACM, pp 3247–3256Google Scholar
  6. Horng D, Chau P, Kittur A, Hong JI, Faloutsos C (2011) Apolo: making sense of large network data by combining rich user interaction and machine learning. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 167–176Google Scholar
  7. Leskovec J, Krevl A (2014) SNAP Datasets: Stanford large network dataset collection.
  8. Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582CrossRefGoogle Scholar
  9. Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci 105(4):1118–1123CrossRefGoogle Scholar
  10. Shi L, Cao N, Liu S, Qian W, Tan L, Wang G, Sun J, Lin CY (2009) Himap: adaptive visualization of large-scale online social networks. In: 2009 IEEE pacific visualization symposium (PacificVis), pp 41–48Google Scholar
  11. Theory C (2010) Patterns of communication.
  12. Tian Y, Hankins RA, Patel JM (2008) Efficient aggregation for graph summarization. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data, SIGMOD ’08, pp 567–580Google Scholar
  13. van den Elzen S, Holten D, Blaas J, van Wijk J (2016) Reducing snapshots to points: a visual analytics approach to dynamic network exploration. IEEE Trans Vis Comput Graph 22(1):1–10CrossRefGoogle Scholar
  14. Vehlow C, Beck F, Auwärter P, Weiskopf D (2015) Visualizing the evolution of communities in dynamic graphs. Comput Graph Forum 34:277–288CrossRefGoogle Scholar
  15. Wernicke S (2006) Efficient detection of network motifs. IEEE/ACM Trans Comput Biol Bioinform 3(4):347–359CrossRefGoogle Scholar
  16. Wickham H (2011) Asa 2009 data expo. J Comput Graph Stat 20(2):281–283MathSciNetCrossRefGoogle Scholar
  17. Wu Y, Wu W, Yang S, Yan Y, Qu H (2015) Interactive visual summary of major communities in a large network. In: 2015 IEEE pacific visualization symposium (PacificVis), pp 47–54Google Scholar
  18. Xu J (2013) Topological structure and analysis of interconnection networks, vol 7. Springer Science and Business MediaGoogle Scholar
  19. Zhang X, Martin T, Newman MEJ (2015) Identification of core-periphery structure in networks. Phys Rev E 91(3):1–10Google Scholar
  20. Zinsmaier M, Brandes U, Deussen O, Strobelt H (2012) Interactive level-of-detail rendering of large graphs. IEEE Trans Vis Comput Graph 18(12):2486–2495CrossRefGoogle Scholar

Copyright information

© The Visualization Society of Japan 2016

Authors and Affiliations

  1. 1.Department of ComputingThe Hong Kong Polytechnic UniversityHong KongPeople’s Republic of China

Personalised recommendations