Fig. 1. Comparison of different GAEs from contrastive learning perspective.

We introduce the lrGAE (left-rigt GAE) benchmark --- graph autoencoders as contrastive learning architectures. lrGAE provides a new contrastive learning perspective of designing powerful GAEs from five dimensions:

• Augmentations
• Encoder/decoder networks
• Contrastive views
• Contrastive losses
• (optional) Negative examples

The contrastive views is the key to design different yet advanced GAEs, which invovels three components: graph views, receptive fields, and node pairs.

• Graph views: the graph or augmentated graph in two contrastive views, denoted as graph $A$ or $B$.
• Receptive fields: the depth of the graph neural network or the number of sampled hops in a node's neighborhood, denoted as $l$ or $r$.
• Node pairs: the contrasting objective over a single node $v$ or two nodes $v$ and $u$.

Therefore, we have $2^3=8$ variants of lrGAE in terms of the contrastive views, shown below:

Table 1. Illustration of all possible cases of GAEs falling within the lrGAE frmework

Before you begin, please make sure that you have Anaconda or Miniconda installed on your system. This guide assumes that you have a CUDA-enabled GPU.

# Create and activate a new Conda environment named 'lrGAE'
conda create -n lrGAE python==3.12 -c conda-forge -y
conda activate lrGAE

# Install Pytorch 2.3.1 with CUDA 12.1 support
# If your use a different CUDA version, please refer to the PyTorch website for the appropriate versions.
pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121

# Install PyG
pip install torch_geometric
# Install additional dependencies of PyG
pip install pyg_lib torch_scatter torch_sparse torch_cluster -f https://data.pyg.org/whl/torch-2.3.0+cu121.html

Additional dependences of PyG can be found at https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html

Please make sure you have installed PyTorch and PyTorch Geometric (PyG).

# Coming soon
pip install -U lrgae

or

# Recommended
git clone https://github.com/EdisonLeeeee/lrGAE.git && cd lrGAE
pip install -e . --verbose

where -e means "editable" mode so you don't have to reinstall every time you make changes.

Five graph-based learning tasks are supported:

• Node classification task
• Link prediction task
• Graph clustering task
• Graph classification task
• Heterogeneous node classification task

• GAE: Variational graph auto-encoders. NeurIPS 2016
• MaskGAE: What’s behind the mask: Understanding masked graph modeling for graph autoencoders. KDD 2023
• GraphMAE: GraphMAE: Self-supervised masked graph autoencoders. KDD 2022
• GraphMAE2: GraphMAE2: A decoding-enhanced masked self-supervised graph learner. WWW 2023
• AUG-MAE: Rethinking graph masked autoencoders through alignment and uniformity. AAAI 2024
• GiGaMAE: GiGaMAE: Generalizable graph masked autoencoder via collaborative latent space reconstruction. CIKM 2023
• S2GAE: S2GAE: self-supervised graph autoencoders are generalizable learners with graph masking. WSDM 2023
• 7 variants of lrGAE in terms of different contrastive views (See Fig.2 for illustration)
  • 2️⃣ lrGAE-ABllvv
  • 3️⃣ lrGAE-AAlrvv
  • 4️⃣ lrGAE-ABlrvv
  • 5️⃣ lrGAE-AAllvu
  • 5️⃣ lrGAE-AAlrvu
  • 7️⃣ lrGAE-ABllvu
  • 8️⃣ lrGAE-ABlrvu

Fig. 2. Illustration of seven possible cases of lrGAE.