Module-based visualization of large-scale graph network data
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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.
KeywordsNetwork 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.
- 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
- 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
- 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 167–176Google Scholar
- Leskovec J, Krevl A (2014) SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data
- 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
- 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 567–580Google Scholar
- 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
- Xu J (2013) Topological structure and analysis of interconnection networks, vol 7. Springer Science and Business MediaGoogle Scholar
- Zhang X, Martin T, Newman MEJ (2015) Identification of core-periphery structure in networks. Phys Rev E 91(3):1–10Google Scholar