document: Mining closed relational graphs with connectivity constraints Relational graphs are widely used in modeling large scale networks such as biological networks and social networks . In this kind of graph , connectivity becomes critical in identifying highly associated groups and clusters . In this paper , we investigate the issues of mining closed frequent graphs with connectivity constraints in massive relational graphs where each graph has around 10K nodes and 1M edges . We adopt the concept of edge connectivity and apply the results from graph theory , to speed up the mining process . Two approaches are developed to handle different mining requests : CloseCut , a pattern-growth approach , and splat , a pattern-reduction approach . We have applied these methods in biological datasets and found the discovered patterns interesting .
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