nightlyjourney / SimGNN

This is the code for the WSDM 2019 paper.

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GraphEmbedding

This version is for the WSDM 2019 paper.

Datasets

Get the datasets from https://drive.google.com/drive/folders/1lY3pqpnUAK0H9Tgjyh7tlMVYy0gYPthC?usp=sharing and extract under data/:

  • AIDS80nef
  • AIDS700nef
  • linux
  • IMDBMulti

Get the pickle files (/save) from https://drive.google.com/drive/folders/1Eusvi4_iOKM0AsO1LhxQFkY62kDEtuMq?usp=sharing

Get the result files (/result) https://drive.google.com/drive/folders/1UXEGozaThjjuC-hnt4C7jn06L6I2Ra1v?usp=sharing

Dependencies

Install the following the tools and packages:

  • python3: Assume python3 by default (use pip3 to install packages).
  • numpy
  • pandas
  • scipy
  • scikit-learn
  • tensorflow (1.8.0 recommended)
  • networkx==1.10 (NOT 2.1)
  • beautifulsoup4
  • lxml
  • matplotlib
  • seaborn
  • colour
  • pytz
  • pygraphviz. The following is an example set of installation commands (tested on Ubuntu 16.04)
    sudo apt-get install graphviz libgraphviz-dev pkg-config
    pip3 install pygraphviz --install-option="--include-path=/usr/include/graphviz" --install-option="--library-path=/usr/lib/graphviz/"
    
  • Graph Edit Distance (GED):

Tips for PyCharm Users

  • If you see red lines under import, mark src and model/Siamese as Source Root, so that PyCharm can find those files.
  • Mark src/Siamese/logs and src/Siamese/exp as Excluded, so that PyCharm won't spend time inspecting those logs.

About

This is the code for the WSDM 2019 paper.


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