FLAG

gtrick.FLAG(emb_dim, loss_func, optimizer, m=3, step_size=0.001, mag=-1)

FLAG is an adversarial data augmentation method for Graph Neural Networks, which comes from Robust Optimization as Data Augmentation for Large-scale Graphs.

This trick is helpful for Node Level Task and Graph Level Task.

Example

FLAG (DGL), FALG (PyG)

Parameters:

Name	Type	Description	Default
emb_dim	int	Node feature dim.	required
loss_func	torch.nn.Module	Loss function.	required
optimizer	torch.optim.Optimizer)	Optimizer.	required
m	int	Ascent steps. Train the same minibatch m times.	3
step_size	float	Ascent step size. If mag <= 0, perturb is initialized from uniform distribution [-step_size, step_size].	0.001
mag	float	If mag > 0, it controls the max norm of perturb.	-1

__call__(model, forward, num_nodes, y)

Parameters:

Name	Type	Description	Default
model	torch.nn.Module	The model.	required
forward	Callable[[torch.Tensor], torch.Tensor]	The function that inputs perturb and gets output.	required
num_nodes	int	The number of nodes.	required
y	torch.Tensor	The ground truth label.	required

Returns:

Type	Description
torch.Tensor	The loss.
torch.Tensor	The output of the model.