pygip.utils

class pygip.utils.GraphNeuralNetworkMetric(fidelity=0, accuracy=0, model=None, graph=None, features=None, mask=None, labels=None, query_labels=None)

Bases: object

Graph Neural Network Metric Class.

This class evaluates two metrics, fidelity and accuracy, for a given GNN model on a specified graph and features.

static calculate_surrogate_fidelity(target_model, surrogate_model, data, mask=None)

Calculate fidelity between target and surrogate model predictions.

Parameters:

• target_model – Original model

• surrogate_model – Extracted surrogate model

• data – Input graph data

• mask – Optional mask for evaluation on specific nodes

Returns:

Fidelity score (percentage of matching predictions)

Return type:

float

evaluate()

Main function to update fidelity and accuracy scores.

evaluate_helper(model, graph, features, labels, mask)

Helper function to evaluate the model’s performance.

static evaluate_surrogate_extraction(target_model, surrogate_model, data, train_mask=None, val_mask=None, test_mask=None)

Comprehensive evaluation of surrogate extraction attack.

Parameters:

• target_model – Original model

• surrogate_model – Extracted surrogate model

• data – Input graph data

• train_mask – Mask for training nodes

• val_mask – Mask for validation nodes

• test_mask – Mask for test nodes

Returns:

Dictionary containing fidelity scores for different data splits

Return type:

dict

Modules

dglTopyg
hardware
metrics