Awesome

Overview

Here we provide the implementation of the NAS-GC in TensorFlow, along with a execution example (on the Cora dataset). The repository is organised as follows:

• data/ contains the graph dataset file (Cora);
• evaluator/ contains the evaluations of clustering (metrics.py);
• model/ contains:
  • evaluating the performance of NAS-GC network (cluster.py);
  • the implementation of self-supervision in loss function (loss_estimator.py);
  • the implementation of the NAS-GC network (repres_learner.py);
  • preparing to train NAS-GC network (trainer.py);
• util/ contains data preprocessing of graph dataset (data_processor.py).

Finally, main.py sets all hyperparameters and may be used to execute a full training run on graph dataset.

$ python main.py

Architecture

Dataset

Tip: Due that the data size is too large, we cannot upload it. But it can be downloaded from https://ogb.stanford.edu/docs/nodeprop/#ogbn-arxiv.

Dependencies

The script has been tested running under Python 3.7.4, with the following packages installed (along with their dependencies):

• numpy==1.18.1
• scipy==1.2.0
• tensorflow-gpu==1.14.0
• scikit-learn==0.22.1

In addition, CUDA 10.0 and cuDNN 7.5 have been used.

References

You may also be interested in the related articles：

• Attributed Graph Clustering via Adaptive Graph Convolution AGC
• Structural Deep Clustering Network SDCN
• Text-associated DeepWalk TADW

License

MIT