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Issue No.01 - Jan. (2013 vol.25)
pp: 106-118
Jose F. Rodrigues, Jr , Universidade de São Paulo, São Carlos
Hanghang Tong , IBM T.J. Watson Research, Hawthorne
Jia-Yu Pan , Google, Inc., Pittsburgh, Pittsburgh
Agma J.M. Traina , Universidade de São Paulo, São Carlos
Caetano Traina, Jr , Universidade de São Paulo, São Carlos
Christos Faloutsos , Carnegie Mellon University, Pittsburgh
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.
Data structures, Visualization, Partitioning algorithms, Communities, Social network services, Computational modeling, Layout, graph visualization, Graph analysis system, graph representation, data structures, graph mining
Jose F. Rodrigues, Jr, Hanghang Tong, Jia-Yu Pan, Agma J.M. Traina, Caetano Traina, Jr, Christos Faloutsos, "Large Graph Analysis in the GMine System", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 1, pp. 106-118, Jan. 2013, doi:10.1109/TKDE.2011.199
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