document: Scalable mining of large disk-based graph databases Mining frequent structural patterns from graph databases is an interesting problem with broad applications . Most of the previous studies focus on pruning unfruitful search subspaces effectively , but few of them address the mining on large , disk-based databases . As many graph databases in applications can not be held into main memory , scalable mining of large , disk-based graph databases remains a challenging problem . In this paper , we develop an effective index structure , ADI ( for ( u ) ad ( \/ u ) jacency ( u ) i ( \/ u ) ndex ) , to support mining various graph patterns over large databases that can not be held into main memory . The index is simple and efficient to build . Moreover , the new index structure can be easily adopted in various existing graph pattern mining algorithms . As an example , we adapt the well-known gSpan algorithm by using the ADI structure . The experimental results show that the new index structure enables the scalable graph pattern mining over large databases . In one set of the experiments , the new disk-based method can mine graph databases with one million graphs , while the original gSpan algorithm can only handle databases of up to 300 thousand graphs . Moreover , our new method is faster than gSpan when both can run in main memory .
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