Subgraph matching

Subgraph matching finds a set I of all occurrences of a pattern graph in a target graph. It has a wide range of applications while suffers an expensive computation. This efficiency issue has been studied extensively. All existing approaches, however, turn a blind eye to the output crisis , that is, when the system has to materialize I as a preprocessing/intermediate/final result or an index, the cost of the export of I dominates the overall cost, which could be prohibitive even for a small pattern graph. This paper studies subgraph matching via two problems. 1) Is there an ideal compression of I ? 2) Will the compression of I reversely boost the computation of I ? For the problem 1), we propose a technique called VCBC to compress I to code ( I ) which serves effectively the same as I. For problem 2), we propose a subgraph matching computation framework CBF which computes code( I )instead of I to bring down the output cost. CBF further reduces the overall cost by reducing the intermediate results. Extensive experiments show that the compression ratio of VCBC can be up to 10 5 which also significantly lowers the output cost of CBF. Extensive experiments show the superior performance of CBF over existing approaches.