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.
To install for all users on Unix/Linux:
python3.4 setup.py build sudo python3.4 setup.py install
User Classification Experiments
- 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
- Implemented methods: ARCTE, BaseComm, LapEig, RepEig, Louvain, MROC.
- Other methods' implementations: LINE, DeepWalk, [EdgeCluster](http://leitang.net/social dimension.html), RWModMax, BigClam, OSLOM.
User classification comparative study results
- Follow instructions on file: reveal_graph_embedding/experiments/demo.py
- If you installed the package, you will have an installed script called arcte.
- The source is located in reveal_graph_embedding/entry_points/arcte.py