Experiments to support paper "Artificial Benchmark for Community Detection with Outliers" in a Python Jupyter Notebook.
- The ABCD+o datasets were built using: https://github.com/bkamins/ABCDGraphGenerator.jl
- The email_EU dataset is taken from: https://snap.stanford.edu/data/email-Eu-core.html
- The Football dataset reference: "Community structure in social and biological networks", M. Girvan and M. E. J. Newman PNAS June 11, 2002 99 (12) 7821-7826; https://doi.org/10.1073/pnas.122653799
The notebook uses mainly common Python packages, with the addition of:
- pip install igraph (see: https://pypi.org/project/igraph/ and https://igraph.readthedocs.io/en/0.10.2/)
- pip install partition-igraph (see: https://pypi.org/project/partition-igraph/)
Files with stored experiment results are supplied for a few (longer) tests, but those can also be re-created. The following tools then need to be installed to run those:
- The node2vec graph embedding code from: https://github.com/snap-stanford/snap/tree/master/examples/node2vec
- The graph embedding divergence (GED) code from: https://github.com/ftheberge/Comparing_Graph_Embeddings
and the full paths to the executables needs to be supplied, as explained in the notebook.