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| # -*- coding: utf-8 -*- | ||
| """Community-Guided Contrastive Learning with Anomaly-Aware Reconstruction for | ||
| Anomaly Detection on Attributed Networks.(CARD) """ | ||
| # Author: Yang Wang<[email protected]> | ||
| # License: BSD 2 clause | ||
|
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| import torch | ||
| from torch_geometric.nn import GCN | ||
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| from .base import DeepDetector | ||
| from ..nn import CARDBase | ||
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| class CARD(DeepDetector): | ||
| """ | ||
| Community-Guided Contrastive Learning with Anomaly-Aware Reconstruction for | ||
| Anomaly Detection on Attributed Networks. | ||
| CARD is a contrastive learning based method and utilizes mask reconstruction and community | ||
| information to make anomalies more distinct. This model is train with contrastive loss and | ||
| local and global attribute reconstruction loss. Random neighbor sampling instead of random walk | ||
| sampling is used to sample the subgraph corresponding to each node. Since random neighbor sampling | ||
| cannot accurately control the number of neighbors for each sampling, it may run slower compared to | ||
| the method implementation in the original paper. | ||
| See:cite:`Wang2024Card` for details. | ||
| Parameters | ||
| ---------- | ||
| hid_dim : int, optional | ||
| Hidden dimension of model. Default: ``64``. | ||
| num_layers : int, optional | ||
| Total number of layers in model. Default: ``2``. | ||
| dropout : float, optional | ||
| Dropout rate. Default: ``0.``. | ||
| weight_decay : float, optional | ||
| Weight decay (L2 penalty). Default: ``0.``. | ||
| act : callable activation function or None, optional | ||
| Activation function if not None. | ||
| Default: ``torch.nn.functional.relu``. | ||
| backbone : torch.nn.Module | ||
| The backbone of the deep detector implemented in PyG. | ||
| Default: ``torch_geometric.nn.GCN``. | ||
| contamination : float, optional | ||
| The amount of contamination of the dataset in (0., 0.5], i.e., | ||
| the proportion of outliers in the dataset. Used when fitting to | ||
| define the threshold on the decision function. Default: ``0.1``. | ||
| lr : float, optional | ||
| Learning rate. Default: ``0.004``. | ||
| epoch : int, optional | ||
| Maximum number of training epoch. Default: ``100``. | ||
| gpu : int | ||
| GPU Index, -1 for using CPU. Default: ``-1``. | ||
| batch_size : int, optional | ||
| Minibatch size, 0 for full batch training. Default: ``0``. | ||
| num_neigh : int, optional | ||
| Number of neighbors in sampling, -1 for all neighbors. | ||
| Default: ``-1``. | ||
| subgraph_num_neigh: int, optional | ||
| Number of neighbors in subgraph sampling for each node, Values not exceeding 4 are recommended for efficiency. | ||
| Default: ``4``. | ||
| fp: float, optional | ||
| The balance parameter between the mask autoencoder module and contrastive learning. | ||
| Default: ``0.6`` | ||
| gama: float, optional | ||
| The proportion of the local reconstruction in contrastive learning module. | ||
| Default: ``0.5`` | ||
| alpha: float, optional | ||
| The proprotion of the community embedding in the conbine_encoder. | ||
| Default: ``0.1`` | ||
| verbose : int, optional | ||
| Verbosity mode. Range in [0, 3]. Larger value for printing out | ||
| more log information. Default: ``0``. | ||
| save_emb : bool, optional | ||
| Whether to save the embedding. Default: ``False``. | ||
| compile_model : bool, optional | ||
| Whether to compile the model with ``torch_geometric.compile``. | ||
| Default: ``False``. | ||
| **kwargs | ||
| Other parameters for the backbone. | ||
| Attributes | ||
| ---------- | ||
| decision_score_ : torch.Tensor | ||
| The outlier scores of the training data. Outliers tend to have | ||
| higher scores. This value is available once the detector is | ||
| fitted. | ||
| threshold_ : float | ||
| The threshold is based on ``contamination``. It is the | ||
| :math:`N \\times` ``contamination`` most abnormal samples in | ||
| ``decision_score_``. The threshold is calculated for generating | ||
| binary outlier labels. | ||
| label_ : torch.Tensor | ||
| The binary labels of the training data. 0 stands for inliers | ||
| and 1 for outliers. It is generated by applying | ||
| ``threshold_`` on ``decision_score_``. | ||
| emb : torch.Tensor or tuple of torch.Tensor or None | ||
| The learned node hidden embeddings of shape | ||
| :math:`N \\times` ``hid_dim``. Only available when ``save_emb`` | ||
| is ``True``. When the detector has not been fitted, ``emb`` is | ||
| ``None``. When the detector has multiple embeddings, | ||
| ``emb`` is a tuple of torch.Tensor. | ||
| """ | ||
|
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||
| def __init__(self, | ||
| hid_dim=64, | ||
| num_layers=2, | ||
| dropout=0., | ||
| weight_decay=0., | ||
| act=torch.nn.functional.relu, | ||
| backbone=GCN, | ||
| contamination=0.1, | ||
| lr=4e-3, | ||
| epoch=100, | ||
| gpu=-1, | ||
| batch_size=0, | ||
| num_neigh=-1, | ||
| subgraph_num_neigh=4, | ||
| fp=0.6, | ||
| gama=0.5, | ||
| alpha=0.1, | ||
| verbose=0, | ||
| save_emb=False, | ||
| compile_model=False, | ||
| **kwargs): | ||
| super(CARD, self).__init__(hid_dim=hid_dim, | ||
| num_layers=num_layers, | ||
| dropout=dropout, | ||
| weight_decay=weight_decay, | ||
| act=act, | ||
| backbone=backbone, | ||
| contamination=contamination, | ||
| lr=lr, | ||
| epoch=epoch, | ||
| gpu=gpu, | ||
| batch_size=batch_size, | ||
| num_neigh=num_neigh, | ||
| verbose=verbose, | ||
| save_emb=save_emb, | ||
| compile_model=compile_model, | ||
| **kwargs) | ||
| self.subgraph_num_neigh = subgraph_num_neigh | ||
| self.fp = fp | ||
| self.gama = gama | ||
| self.alpha = alpha | ||
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| def process_graph(self, data): | ||
| community_adj, self.diff_data = CARDBase.process_graph(data) | ||
| data.community_adj = community_adj.to(self.device) | ||
| self.diff_data = self.diff_data.to(self.device) | ||
| self.diff_data.community_adj = community_adj.to(self.device) | ||
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| def init_model(self, **kwargs): | ||
| if self.save_emb: | ||
| self.emb = torch.zeros(self.num_nodes, | ||
| self.hid_dim) | ||
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| return CARDBase(in_dim=self.in_dim, | ||
| subgraph_num_neigh=self.subgraph_num_neigh, | ||
| fp=self.fp, | ||
| gama=self.gama, | ||
| alpha=self.alpha, | ||
| hid_dim=self.hid_dim, | ||
| num_layers=self.num_layers, | ||
| dropout=self.dropout, | ||
| act=self.act, | ||
| backbone=self.backbone, | ||
| **kwargs).to(self.device) | ||
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| def forward_model(self, data): | ||
| batch_size = data.batch_size | ||
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| data.x = data.x.to(self.device) | ||
| data.edge_index = data.edge_index.to(self.device) | ||
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| pos_logits, neg_logits, x_, local_x_ = self.model(data) | ||
| diff_pos_logits, diff_neg_logits, _, _ = self.model( | ||
| self.diff_data) | ||
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| logits = torch.cat([pos_logits[:batch_size], | ||
| neg_logits[:batch_size]]) | ||
| diff_logits = torch.cat([diff_pos_logits[:batch_size], | ||
| diff_neg_logits[:batch_size]]) | ||
|
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| con_label = torch.cat([torch.ones(batch_size), | ||
| torch.zeros(batch_size)]).to(self.device) | ||
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| loss, score = self.model.loss_func( | ||
| logits, diff_logits, x_, local_x_, data.x, con_label) | ||
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| return loss, score.detach().cpu() |
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