The program implements FFSM algorithm from paper [1]. Basically, it finds frequent patterns that occur in given graph database and send it out to standard output.

Input: It accepts an integer as a minsup and a database of graphs from standard input.

Output: It prints out frequent graphs to standard output.

In the program, frequency of a pattern is defined as total number of its instance in all the graphs. That means one graph may contain multiple number of the same pattern. And one node in an instance of a subgraph could form another instance of the same subgraph as well. That is to say, two instances of a same pattern in a graph may have overlapping nodes and edges. Subgraphs whose frequency is larger or equal to minsup is considered as frequent pattern.

There is also another widely accepted technology that counts frequency as the number of graphs that contain certain subgraph. The method is included in the source code of this project as well. Please contact me if need further detail.

The frequent patterns is send to standard output in the form of both matrix and canonical label.

[1]: Huan, Jun, Wei Wang, and Jan Prins. "Efficient mining of frequent subgraphs in the presence of isomorphism." Data Mining, 2003. ICDM 2003. Third IEEE International Conference on. IEEE, 2003. (pdf)