This repository is the official implementation of Strategic Classification with Graph Neural Networks.
This project is based on PyTorch 1.8.0 and the PyTorch Geometric library.
First, install PyTorch from the official website: https://pytorch.org/. Then install PyTorch Geometric: https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html (PyTorch Geometric must be installed according to the instructions there).
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synthetic_dataset_main Returns the non-strategic, naive and robust accuracies for a single experiment, performed on our synthetic dataset.
The available input arguments are:
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--alpha: The influence of the graph. -
--cost_lim: The maximal moving distance. -
--train_iterations: The limit on the number of train iterations. If not set, then we iterate until we converge. -
--test_iterations: The limit on the number of test iterations. If not set, then we iterate until we converge. -
--seed: A seed for reproducability.
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real_datasets_main Returns the non-strategic, naive and robust accuracies for a single experiment, performed on a real dataset (Cora, CiteSeer or PubMed).
The available input arguments are:
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--dataset: Name of the real dataset, all caps. -
--num_layers: The number of layers in the SGC model. -
--cost_lim: The maximal moving distance. -
--train_iterations: The limit on the number of train iterations. If not set, then we iterate until we converge. -
--temp: The sigmoid temperature. -
--lr: The learning rate. -
--epochs: The number of training epochs. -
--decay: The weight decay. -
--seed: A seed for reproducability.