PlanetAlign.algorithms.FINAL

class FINAL(alpha: float = 0.5, dtype: dtype = torch.float32)[source]

Bases: BaseModel

Consistency-based method FINAL for pairwise attributed network alignment. FINAL is proposed by the paper “FINAL: Fast Attributed Network Alignment” in KDD 2016.

Parameters:

  • alpha (float, optional) – The regularization parameter in the optimization objective. Default is 0.5.

  • dtype (torch.dtype, optional) – Data type of the tensors, choose from torch.float32 or torch.float64. Default is torch.float32.

test(dataset: Dataset, gids: List[int] | Tuple[int, ...], metrics: tuple[str] | list[str] = None)
Parameters:

  • dataset (Dataset) – The dataset containing the graphs to be aligned and the training/test data.

  • gids (list[int] or tuple[int, ...]) – The indices of the graphs in the dataset to be aligned.

  • metrics (tuple[str] or list[str], optional) – The metrics to be computed after alignment. Default is None, which computes Hits@K (K=1, 10, 30, 50) and MRR metrics.

train(dataset: Dataset, gid1: int, gid2: int, use_attr: bool = True, total_epochs: int = 50, tol: float = 1e-10, save_log: bool = True, verbose: bool = True)[source]
Parameters:

  • dataset (Dataset) – The dataset containing the graphs to be aligned and the training/test data.

  • gid1 (int) – The index of the first graph in the dataset to be aligned.

  • gid2 (int) – The index of the second graph in the dataset to be aligned.

  • use_attr (bool, optional) – Whether to use node and edge attributes for alignment. Default is True.

  • total_epochs (int, optional) – The maximum number of epochs for the optimization. Default is 50.

  • tol (float, optional) – The tolerance for convergence. Default is 1e-10.

  • save_log (bool, optional) – Whether to save the log of the training process. Default is True.

  • verbose (bool, optional) – Whether to print the progress during training. Default is True.