SelfGNN

A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which will appear in The International Workshop on Self-Supervised Learning for the Web (SSL'21) @ the Web Conference 2021 (WWW'21).

Note

This is an ongoing work and the repository is subjected to continuous updates.

Requirements!

• Python 3.6+
• PyTorch 1.6+
• PyTorch Geometric 1.6+
• Numpy 1.17.2+
• Networkx 2.3+
• SciPy 1.5.4+
• (OPTINAL) OPTUNA 2.8.0+ If you wish to tune the hyper-parameters of SelfGNN for any dataset

Example usage

$ python src/train.py

:collision: Updates

Update 3

Added a hyper-parameter tuning utility using OPTUNA.

usage:

$ python src/tune.py

Update 2

Contrary to what we've claimed in the paper, studies argue and empirically show that Batch Norm does not introduce implicit negative samples. Instead, mainly it compensate for improper initialization. We have carried out new and similar experiments, as shown in the table below, that seems to confirm this argument. (BN:Batch Norm, LN:Layer Norm, -: No Norm ). For this experiment we use a GCN encoder and split data-augmentation. Though BN does not provide implicit negative samples, the empirical evaluation shows that it leads to a better performance; putting it in the encoder is almost sufficient. LN on the other hand is not cosistent; furthemore, the model tends to prefer having BN than LN in any of the modules.

Update 1

• Both the paper and the source code are updated following the discussion on this issue
• Ablation study on the impact of BatchNorm added following reviewers feedback from SSL'21
  • The findings show that SelfGNN with out batch normalization is not stable and often its performance drops significantly
  • Layer Normalization behaves similar to the finding of no BatchNorm

Possible options for training SelfGNN

The following options can be passed to src/train.py

--root: or -r: A path to a root directory to put all the datasets. Default is ./data

--name: or -n: The name of the datasets. Default is cora. Check the Supported dataset names

--model: or -m: The type of GNN architecture to use. Curently three architectres are supported (gcn, gat, sage). Default is gcn.

--aug: or -a: The name of the data augmentation technique. Curently (ppr, heat, katz, split, zscore, ldp, paste) are supported. Default is split.

--layers: or -l: One or more integer values specifying the number of units for each GNN layer. Default is 512 128

--norms: or -nm: The normalization scheme for each module. Default is batch. That is, a Batch Norm will be used in the prediction head. Specifying two inputs, e.g. --norms batch layer, allows the model to use batch norm in the GNN encoder, and layer norm in the prediction head. Finally, specifying three inputs, e.g., --norms no batch layer activates the projection head and normalization is used as: No norm for GNN encoder, Batch Norm for projection head and Layer Norm for the prediction head.

--heads: or -hd: One or more values specifying the number of heads for each GAT layer. Applicable for --model gat. Default is 8 1

--lr: or -lr: Learning rate, a value in [0, 1]. Default is 0.0001

--dropout: or -do: Dropout rate, a value in [0, 1]. Deafult is 0.2

--epochs: or -e: The number of epochs. Default is 1000.

--cache-step: or -cs: The step size for caching the model. That is, every --cache-step the model will be persisted. Default is 100.

--init-parts: or -ip: The number of initial partitions, for using the improved version using Clustering. Default is 1.

--final-parts: or -fp: The number of final partitions, for using the improved version using Clustering. Default is 1.

Supported dataset names

Any dataset from the PyTorch Geometric library can be used, however SelfGNN is tested only on the above datasets.

Citing

If you find this research helpful, please cite it as

@misc{kefato2021selfsupervised,
      title={Self-supervised Graph Neural Networks without explicit negative sampling}, 
      author={Zekarias T. Kefato and Sarunas Girdzijauskas},
      year={2021},
      eprint={2103.14958},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}