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graph-representation-july's Introduction

Graph-Representation-july

The Tensorflow Implementation based on CogDL
This is just a simple reimplementation for practice! Note that this task is still actively under development, so feedback and contributions are welcome. The results seem to differ from the CogDL, hope for any ideas!


Author LCorleone
E-mail [email protected]

Requirements

  • tensorflow 2
  • keras 2.3.1
  • networkx

Usage

  • All parameters can be set in para_config.py.
  • In main.py, set model, dataset and task and run main.py.

Support

  • Unsupervised node classification for dataset: blogcatalog, PPI and Wikipedia.
  • node classification for dataset: cora.

Results

  • Unsupervised node classification (Micro-F1 0.9)

Algorithm wikipedia blogcatalog
line 0.464 Todo
netmf 0.418 0.352
grarep Todo 0.386
hope Todo 0.376
deepwalk Todo 0.388
node2vec Todo 0.387
prone Todo 0.425

  • node classification (Acc)

Algorithm cora
GCN 0.81

Reference

keras-gcn

graph-representation-july's People

Contributors

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Stargazers

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