José F. Rodrigues
Christos Faloutsos
Jure Leskovec
ABSTRACT
Several graph visualization tools exist. However, they are not able to handle large graphs, and/or they do not allow interaction. We are interested on large graphs, with hundreds of thousands of nodes. Such graphs bring two challenges: the first one is that any straightforward interactive manipulation will be prohibitively slow. The second one is sensory overload: even if we could plot and replot the graph quickly, the user would be overwhelmed with the vast volume of information because the screen would be too cluttered as nodes and edges overlap each other.Our GMine system addresses both these issues, by using summarization and multi-resolution. GMine offers multi-resolution graph exploration by partitioning a given graph into a hierarchy of communities-within-communities and storing it into a novel R-treelike structure which we name G-Tree. GMine offers summarization by implementing an innovative subgraph extraction algorithm and then visualizing its output.
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Digital Library
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- {4} J. Leskovec, A. Singh, and J. Kleinberg. Patterns of influence in a recommendation network. In PAKDD, volume 3918, pages 380-389. Springer-Verlag, 2006. Google ScholarDigital Library
- {5} R. Matthew, R. Agrawal, and P. Domingos. Trust management for the semantic web. In 2nd ISWC, pages 351-368, 2003.Google Scholar
Index Terms
- GMine: a system for scalable, interactive graph visualization and mining
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