Abstract
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
Similar content being viewed by others
References
Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10008
Bostock M, Ogievetsky V, Heer J (2011) D3: data-driven documents. IEEE Trans Vis Comput Graph 17(12):2301–2309
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 43:1–43:8
Dorogovtsev SN, Goltsev AV, Mendes JFF (2006) \(k\)-core organization of complex networks. Phys Rev Lett 96:040601
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 3247–3256
Google: color palette. https://www.google.com/design/spec/style/color.html
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 167–176
Leskovec J, Krevl A (2014) SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data
Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582
Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci 105(4):1118–1123
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 41–48
Theory C (2010) Patterns of communication. http://communicationtheory.org/patterns-of-communication
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 567–580
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):1–10
Vehlow C, Beck F, Auwärter P, Weiskopf D (2015) Visualizing the evolution of communities in dynamic graphs. Comput Graph Forum 34:277–288
Wernicke S (2006) Efficient detection of network motifs. IEEE/ACM Trans Comput Biol Bioinform 3(4):347–359
Wickham H (2011) Asa 2009 data expo. J Comput Graph Stat 20(2):281–283
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 47–54
Xu J (2013) Topological structure and analysis of interconnection networks, vol 7. Springer Science and Business Media
Zhang X, Martin T, Newman MEJ (2015) Identification of core-periphery structure in networks. Phys Rev E 91(3):1–10
Zinsmaier M, Brandes U, Deussen O, Strobelt H (2012) Interactive level-of-detail rendering of large graphs. IEEE Trans Vis Comput Graph 18(12):2486–2495
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
An earlier version of the paper was presented at the SIGGRAPH Asia 2015 Symposium on Visualization in High Performance Computing, 2–5 November, Kobe, Japan, 2015.
Rights and permissions
About this article
Cite this article
Li, C., Baciu, G. & Wang, Y. Module-based visualization of large-scale graph network data. J Vis 20, 205–215 (2017). https://doi.org/10.1007/s12650-016-0375-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12650-016-0375-5