Module-based visualization of large-scale graph network data

Chenhui Li, George Baciu, Yunzhe Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


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[Figure not available: see fulltext.]
Original languageEnglish
Pages (from-to)205-215
Number of pages11
JournalJournal of Visualization
Issue number2
Publication statusPublished - 1 May 2017


  • Community detection
  • Graph drawing
  • Information visualization
  • Module grouping
  • Network visualization

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Electrical and Electronic Engineering

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