1. In order to compile the code the C++ Boost Libraries are needed. After downloading the Boost libraries, edit the Makefile with the correct location. 2. Download the gSpan binary (http://www.cs.ucsb.edu/~xyan/software/gSpan.htm) and store itin bin/. 3. Rename the desired data folder to "data". 4. You can use the test_driver.py in bin/ to run the code or use the command: ./gload <frequency> <u/d> <Max num neighbors> <data_source_name> <gSpan freq> 5. The frequent subgraphs results for the batches will be in freqfile.graph, or in the logs directory if the test_driver.py is used. 6. Clear the cur_exp/ before running StreamFSM.