dig.fairgraph.method

Fairgraph Methods

Graphair

A fair graph representation method for graph datasets under dig.fairgraph.dataset.fairgraph_dataset implemented from the paper LEARNING FAIR GRAPH REPRESENTATIONS VIA AUTOMATED DATA AUGMENTATIONS.

graphair

This class implements the Graphair model
class graphair(aug_model, f_encoder, sens_model, classifier_model, lr=0.0001, weight_decay=1e-05, alpha=20, beta=0.9, gamma=0.7, lam=1, dataset='POKEC', num_hidden=64, num_proj_hidden=64)[source]

This class implements the Graphair model

Parameters

  • aug_model (torch.nn.Module) – The augmentation model g described in the paper used for automated graph augmentations

  • f_encoder (torch.nn.Module) –

    The represnetation encoder f described in the paper used for contrastive learning

  • sens_model (torch.nn.Module) –

    The adversary model k described in the paper used for adversarial learning

  • classifier_model (torch.nn.Module) – The classifier used to predict the sensitive label of nodes on the augmented graph data.

  • lr (float,optional) – Learning rate for aug_model, f_encoder and sens_model. Defaults to 1e-4

  • weight_decay (float,optional) – Weight decay for regularization. Defaults to 1e-5

  • alpha (float,optional) –

    The hyperparameter alpha used in the paper to scale adversarial loss component. Defaults to 20.0

  • beta (float,optional) –

    The hyperparameter beta used in the paper to scale contrastive loss component. Defaults to 0.9

  • gamma (float,optional) –

    The hyperparameter gamma used in the paper to scale reconstruction loss component. Defaults to 0.7

  • lam (float,optional) –

    The hyperparameter lambda used in the paper to compute reconstruction loss component. Defaults to 1.0

  • dataset (str,optional) – The name of the dataset being used. Used only for the model’s output path. Defaults to ‘POKEC’

  • num_hidden (int,optional) – The input dimension for the MLP networks used in the model. Defaults to 64

  • num_proj_hidden (int,optional) – The output dimension for the MLP networks used in the model. Defaults to 64

Runner

run

This class instantiates Graphair model and implements method to train and evaluate.
class run[source]

This class instantiates Graphair model and implements method to train and evaluate.

run(device, dataset, model='Graphair', epochs=10000, test_epochs=1000, lr=0.0001, weight_decay=1e-05)[source]

This method runs training and evaluation for a fairgraph model on the given dataset. Check examples.fairgraph.Graphair.run_graphair_nba.py for examples on how to run the Graphair model.

Parameters

  • device (torch.device) – Device for computation.

  • model (str, optional) – Defaults to Graphair. (Note that at this moment, only Graphair is supported)

  • dataset (object) – The dataset to train on. Should be one of dig.fairgraph.dataset.fairgraph_dataset.POKEC or dig.fairgraph.dataset.fairgraph_dataset.NBA.

  • epochs (int, optional) – Number of epochs to train on. Defaults to 10_000.

  • test_epochs (int,optional) – Number of epochs to train the classifier while running evaluation. Defaults to 1_000.

  • lr (float,optional) – Learning rate. Defaults to 1e-4.

  • weight_decay (float, optional) – Weight decay factor for regularization. Defaults to 1e-5.

Raise

Exception when model is not Graphair. At this moment, only Graphair is supported.