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);
- evaluating the performance of NAS-GC network (
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
| Name | Nodes | Edges | Features | Classes |
|---|---|---|---|---|
| Cora | 2,708 | 5,429 | 1,433 | 7 |
| Citeseer | 3,327 | 4,732 | 3,703 | 6 |
| Pubmed | 19,717 | 44,338 | 500 | 3 |
| Wiki | 2,405 | 17,981 | 4,973 | 17 |
| ogbn-arxiv | 169,343 | 1,166,243 | 128 | 40 |
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.
The script has been tested running under Python 3.7.4, with the following packages installed (along with their dependencies):
numpy==1.18.1scipy==1.2.0tensorflow-gpu==1.14.0scikit-learn==0.22.1
In addition, CUDA 10.0 and cuDNN 7.5 have been used.
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
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