Source code for pygod.nn.cola

import torch
import torch.nn as nn
from torch_geometric.nn import GCN
from torch.nn.functional import binary_cross_entropy_with_logits

[docs] class CoLABase(nn.Module): """ Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning CoLA is a contrastive self-supervised learning based method for graph anomaly detection. This implementation is base on random neighbor sampling instead of random walk sampling in the original paper. See :cite:`liu2021anomaly` for details. Parameters ---------- in_dim : int Input dimension of model. hid_dim : int, optional Hidden dimension of model. Default: ``64``. num_layers : int, optional Total number of layers in model. Default: ``4``. dropout : float, optional Dropout rate. 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``. **kwargs Other parameters for the backbone. """ def __init__(self, in_dim, hid_dim=64, num_layers=4, dropout=0., act=torch.nn.functional.relu, backbone=GCN, **kwargs): super(CoLABase, self).__init__() self.encoder = backbone(in_channels=in_dim, hidden_channels=hid_dim, num_layers=num_layers, out_channels=hid_dim, dropout=dropout, act=act, **kwargs) self.discriminator = nn.Bilinear(in_dim, hid_dim, 1) self.loss_func = binary_cross_entropy_with_logits self.emb = None
[docs] def forward(self, x, edge_index): """ Forward computation. Parameters ---------- x : torch.Tensor Input attribute embeddings. edge_index : torch.Tensor Edge index. Returns ------- logits : torch.Tensor Discriminator logits of positive examples. neg_logits : torch.Tensor Discriminator logits of negative examples. """ self.emb = self.encoder(x, edge_index) logits = self.discriminator(x, self.emb) perm_idx = torch.randperm(x.shape[0]).to(x.device) neg_logits = self.discriminator(x[perm_idx], self.emb) return logits.squeeze(), neg_logits.squeeze()