I have released the source code of gSpan with parallel computing. This work was published in SDM 2018 and ICCSAMA 2015.
Given a set of graphs (or a graph dataset), the algorithm will find a set of subgraphs whose frequencies are not less than a threshold. Using frequent subgraphs is very useful in many data mining and machine learning tasks such as pattern discovery, classification, and clustering. It also has a variety of real-world applications in different domains, e.g., bio-informatics, chemo-informatics, social networks, action recognition, and so on.
If you have any trouble when running the code, please feel free to let me know.