• Search by Module
• Search by Words
• Search Projects

• Java
• Python
• JavaScript
• TypeScript
• C++
• Scala
• Blog

Project: reveal-graph-embedding (GitHub Link)

• reveal-graph-embedding-master
  • LICENSE
  • reveal_graph_embedding
    • datautil
      • prediction_rw_util.py
      • insight_datautil
        • insight_read_data.py
        • __init__.py
      • make_directory_tree.py
      • asu_datautil
        • asu_read_data.py
        • __init__.py
      • feature_rw_util.py
      • datarw.py
      • score_rw_util.py
      • __init__.py
      • read_exotic_features.py
      • snow_datautil
        • __init__.py
        • snow_read_data.py
    • entry_points
      • arcte.py
      • __init__.py
    • eps_randomwalk
      • push.py
      • cython_opt
        • transition.cpython-34m.so
        • transition.pyx
        • __init__.py
        • push.pyx
        • similarity.pyx
      • similarity.py
      • transition.py
      • __init__.py
    • learning
      • evaluation.py
      • classification.py
      • __init__.py
      • holdout.py
    • experiments
      • demo.py
      • utility.py
      • __init__.py
    • quality
      • conductance.py
      • __init__.py
    • common.py
    • __init__.py
    • embedding
      • competing_methods.py
      • laplacian.py
      • implicit.py
      • text_graph.py
      • arcte
        • cython_opt
          • arcte.pyx
          • __init__.py
        • arcte.py
        • __init__.py
      • community_weighting.py
      • common.py
      • __init__.py
  • setup.py
  • README.md
  • snow2014graph
  • .gitignore
  • MANIFEST.in

reveal-graph-embedding

Implementation of community-based graph embedding for user classification.

Features

• 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.

Install

Required packages

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

Installation

To install for all users on Unix/Linux:

python3.4 setup.py build
sudo python3.4 setup.py 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

• 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

• Link

Experiments demo

• Follow instructions on file: reveal_graph_embedding/experiments/demo.py

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/arcte.py

Citation

If you find this code useful and use it in your research, please acknowledge its use and cite the following paper: Rizos, G., Papadopoulos, S., & Kompatsiaris, Y. (2017). Multilabel user classification using the community structure of online networks. PloS one, 12(3), e0173347.