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Mining labeled subgraph is a popular research task in data mining because of its potential application in many different scientific domains. All the existing methods for this task explicitly or implicitly solve the subgraph isomorphism task which is computationally expensive, so they suffer from the lack of scalability problem when the graphs in the input database are large. In this work, we propose FS^3, which is a sampling based method. It mines a small collection of subgraphs that are most frequent in the probabilistic sense. FS^3 performs a Markov chain Monte Carlo (MCMC) sampling over the space of a fixed-size subgraphs such that the potentially frequent subgraphs are sampled more often. Besides, FS^3 is equipped with an innovative queue manager. It stores the sampled subgraph in a finite queue over the course of mining in such a manner that the top-k positions in the queue contain the most frequent subgraphs. Our experiments on database of large graphs show that FS^3 is efficient, and it obtains subgraphs that are the most frequent amongst the subgraphs of a given size.


I use mpc makefile creator to generate makefile. Please read the article in the link to learn more.

If you have added new files, please change the mpc file accordingly and then run the following command to generate new makefile.

chmod +x
./ -type make codes/randomminer.mwc

Run the following command in randommining/codes folder:


A Sample Run:

./randomminer -d mutagen_2.interactive (data set)  -i 100 (number of iteration)  -s 6 (subgraph size) -q  100000 (queue size)


If you are using the code for research purposes, please consider citing the following paper:

  title={FS3: A sampling based method for top-k frequent subgraph mining},
  author={Saha, Tanay Kumar and Al Hasan, Mohammad},
  journal={Statistical Analysis and Data Mining: The ASA Data Science Journal},
  publisher={Wiley Online Library}