Implementation of community-based graph embedding for user classification.
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Implementation of community-based graph embedding for user classification.


  • Graph-based, multi-label user classification experiment demo.
  • Implementation of the ARCTE (Absorbing Regularized Commute Times Embedding) algorithm for graph-based feature extraction.
  • Implementation of other feature extraction methods for graphs (Laplacian Eigenmaps, Louvain, MROC).
  • Evaluation score and time benchmarks.


Required packages

  • numpy
  • scipy
  • h5py
  • scikit-learn
  • Cython
  • networkx
  • python-louvain


To install for all users on Unix/Linux:

python3.4 build
sudo python3.4 install

User Classification Experiments

Get datasets

  • SNOW2014Graph dataset: Included anonymized in this project.
  • The Arizona State University social computing data repository contains the ASU-Flickr and ASU-YouTube datasets.
  • The Insight Project Resources repository contains the Multiview datasets in which the PoliticsUK dataset can be found.

Feature extraction methods

User classification comparative study results

Experiments demo

  • Follow instructions on file: reveal_graph_embedding/experiments/

ARCTE algorithm

  • If you installed the package, you will have an installed script called arcte.
  • The source is located in reveal_graph_embedding/entry_points/