Large graph analysis in the GMine system

Jose F. Rodrigues, Hanghang Tong, Jia Yu Pan, Agma J.M. Traina, Caetano Traina, Christos Faloutsos

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers, and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates 1) a representation for graphs organized as hierarchies of partitions—the concepts of SuperGraph and Graph-Tree; and 2) a graph summarization methodology—CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally.

Original languageEnglish (US)
Article number6025354
Pages (from-to)106-118
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number1
StatePublished - 2013
Externally publishedYes


  • Graph analysis system
  • data structures
  • graph mining
  • graph representation
  • graph visualization

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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