gammagl.models.CoGSLModel

class CoGSLModel(num_feature, cls_hid, num_class, gen_hid, mi_hid, com_lambda_v1, com_lambda_v2, lam, alpha, cls_dropout, ve_dropout, tau, ggl, big, batch)[source]

CoGSL Model proposed in ‘“Compact Graph Structure Learning via Mutual Information Compression” <https://arxiv.org/pdf/2201.05540.pdf>’_ paper.

Parameters:

  • num_feature (int) – input feature dimension.

  • cls_hid (int) – Classification hidden dimension.

  • num_class (int) – number of classes.

  • gen_hid (int) – GenView hidden dimension.

  • mi_hid (int) – Mi_NCE hidden dimension.

  • com_lambda_v1 (float) – hyperparameter used to generate estimated view 1.

  • com_lambda_v2 (float) – hyperparameter used to generate estimated view 2.

  • lam (float) – hyperparameter used to fusion views.

  • alpha (float) – hyperparameter used to fusion views.

  • cls_dropout (float) – Classification dropout rate.

  • ve_dropout (float) – View_Estimator dropout rate.

  • tau (float) – hyperparameter used to generate sim_matrix to get mi loss.

  • ggl (bool) – whether to use gcnconv of gammagl.

  • big (bool) – whether the dataset is too big.

  • batch (int) – determine the sampling size when the dataset is too big.

get_view(data)[source]
get_mi_loss(feat, views)[source]
get_cls_loss(v1, v2, feat)[source]
get_v_cls_loss(v, feat)[source]
get_fusion(v1, prob_v1, v2, prob_v2)[source]