这个实现包括三个主要部分：H2FDetector_layer、MultiRelationH2FDetectorLayer 和 H2FDetector。每个部分都有其独特的功能和职责。下面是这些组件的详细实现和解释。

1. H2FDetector_layer 类

这是一个基本的 GNN 层，处理图卷积和注意力机制。

• 这是基本的图卷积层，包含注意力机制和关系感知的边签名计算。

class H2FDetector_layer(nn.Module):def __init__(self, input_dim, output_dim, head, relation_aware, etype, dropout, if_sum=False):super().__init__()self.etype = etypeself.head = headself.hd = output_dimself.if_sum = if_sumself.relation_aware = relation_awareself.w_liner = nn.Linear(input_dim, output_dim * head)self.atten = nn.Linear(2 * self.hd, 1)self.relu = nn.ReLU()self.leakyrelu = nn.LeakyReLU()self.softmax = nn.Softmax(dim=1)def forward(self, g, h):with g.local_scope():g.ndata['feat'] = hg.apply_edges(self.sign_edges, etype=self.etype)h = self.w_liner(h)g.ndata['h'] = hg.update_all(message_func=self.message, reduce_func=self.reduce, etype=self.etype)out = g.ndata['out']return outdef message(self, edges):src = edges.srcsrc_features = edges.data['sign'].view(-1, 1) * src['h']src_features = src_features.view(-1, self.head, self.hd)z = torch.cat([src_features, edges.dst['h'].view(-1, self.head, self.hd)], dim=-1)alpha = self.atten(z)alpha = self.leakyrelu(alpha)return {'atten': alpha, 'sf': src_features}def reduce(self, nodes):alpha = nodes.mailbox['atten']sf = nodes.mailbox['sf']alpha = self.softmax(alpha)out = torch.sum(alpha * sf, dim=1)if not self.if_sum:out = out.view(-1, self.head * self.hd)else:out = out.sum(dim=-2)return {'out': out}def sign_edges(self, edges):src = edges.src['feat']dst = edges.dst['feat']score = self.relation_aware(src, dst)return {'sign': torch.sign(score)}

这里是对 H2FDetector_layer 类的详细解释。这个类定义了一个图神经网络（GNN）层，它使用注意力机制来对图中的节点进行特征提取和更新。下面是对每一部分代码的详细解释。

class H2FDetector_layer(nn.Module):def __init__(self, input_dim, output_dim, head, relation_aware, etype, dropout, if_sum=False):super().__init__()self.etype = etypeself.head = headself.hd = output_dimself.if_sum = if_sumself.relation_aware = relation_awareself.w_liner = nn.Linear(input_dim, output_dim * head)self.atten = nn.Linear(2 * self.hd, 1)self.relu = nn.ReLU()self.leakyrelu = nn.LeakyReLU()self.softmax = nn.Softmax(dim=1)

2.

def forward(self, g, h):with g.local_scope():g.ndata['feat'] = hg.apply_edges(self.sign_edges, etype=self.etype)h = self.w_liner(h)g.ndata['h'] = hg.update_all(message_func=self.message, reduce_func=self.reduce, etype=self.etype)out = g.ndata['out']return out

3.

def message(self, edges):src = edges.srcsrc_features = edges.data['sign'].view(-1, 1) * src['h']src_features = src_features.view(-1, self.head, self.hd)z = torch.cat([src_features, edges.dst['h'].view(-1, self.head, self.hd)], dim=-1)alpha = self.atten(z)alpha = self.leakyrelu(alpha)return {'atten': alpha, 'sf': src_features}

4.

def reduce(self, nodes):alpha = nodes.mailbox['atten']sf = nodes.mailbox['sf']alpha = self.softmax(alpha)out = torch.sum(alpha * sf, dim=1)if not self.if_sum:out = out.view(-1, self.head * self.hd)else:out = out.sum(dim=-2)return {'out': out}

5.

def sign_edges(self, edges):src = edges.src['feat']dst = edges.dst['feat']score = self.relation_aware(src, dst)return {'sign': torch.sign(score)}

6.

2. RelationAware 类

这是一个关系感知的模块，用于计算边的关系权重。

• 关系感知模块，用于计算边的关系权重。

class RelationAware(nn.Module):def __init__(self, input_dim, output_dim, dropout):super().__init__()self.d_liner = nn.Linear(input_dim, output_dim)self.f_liner = nn.Linear(3 * output_dim, 1)self.tanh = nn.Tanh()self.dropout = nn.Dropout(dropout)def forward(self, src, dst):src = self.d_liner(src)dst = self.d_liner(dst)diff = src - dste_feats = torch.cat([src, dst, diff], dim=1)e_feats = self.dropout(e_feats)score = self.f_liner(e_feats).squeeze()score = self.tanh(score)return score

RelationAware 类是一个关系感知模块，用于计算图中边的关系权重。它通过处理源节点和目标节点的特征，生成一个关系得分。这个模块在图神经网络（GNN）中常用于捕捉节点之间的关系，从而增强模型的表达能力。
1.

class RelationAware(nn.Module):def __init__(self, input_dim, output_dim, dropout):super().__init__()self.d_liner = nn.Linear(input_dim, output_dim)self.f_liner = nn.Linear(3 * output_dim, 1)self.tanh = nn.Tanh()self.dropout = nn.Dropout(dropout)

2.

3. MultiRelationH2FDetectorLayer 类

这是一个处理多种关系的 GNN 层。

• 处理多种关系的图卷积层，包含对不同关系类型的处理逻辑。

class MultiRelationH2FDetectorLayer(nn.Module):def __init__(self, input_dim, output_dim, head, dataset, dropout, if_sum=False):super().__init__()self.relation = copy.deepcopy(dataset.etypes)self.relation.remove('homo')self.n_relation = len(self.relation)if not if_sum:self.liner = nn.Linear(self.n_relation * output_dim * head, output_dim * head)else:self.liner = nn.Linear(self.n_relation * output_dim, output_dim)self.relation_aware = RelationAware(input_dim, output_dim * head, dropout)self.minelayers = nn.ModuleDict()self.dropout = nn.Dropout(dropout)for e in self.relation:self.minelayers[e] = H2FDetector_layer(input_dim, output_dim, head, self.relation_aware, e, dropout, if_sum)def forward(self, g, h):hs = []for e in self.relation:he = self.minelayers[e](g, h)hs.append(he)h = torch.cat(hs, dim=1)h = self.dropout(h)h = self.liner(h)return hdef loss(self, g, h):with g.local_scope():g.ndata['feat'] = hagg_h = self.forward(g, h)g.apply_edges(self.score_edges, etype='homo')edges_score = g.edges['homo'].data['score']edge_train_mask = g.edges['homo'].data['train_mask'].bool()edge_train_label = g.edges['homo'].data['label'][edge_train_mask]edge_train_pos = edge_train_label == 1edge_train_neg = edge_train_label == -1edge_train_pos_index = edge_train_pos.nonzero().flatten().detach().cpu().numpy()edge_train_neg_index = edge_train_neg.nonzero().flatten().detach().cpu().numpy()edge_train_pos_index = np.random.choice(edge_train_pos_index, size=len(edge_train_neg_index))index = np.concatenate([edge_train_pos_index, edge_train_neg_index])index.sort()edge_train_score = edges_score[edge_train_mask]# hinge lossedge_diff_loss = hinge_loss(edge_train_label[index], edge_train_score[index])train_mask = g.ndata['train_mask'].bool()train_h = agg_h[train_mask]train_label = g.ndata['label'][train_mask]train_pos = train_label == 1train_neg = train_label == 0train_pos_index = train_pos.nonzero().flatten().detach().cpu().numpy()train_neg_index = train_neg.nonzero().flatten().detach().cpu().numpy()train_neg_index = np.random.choice(train_neg_index, size=len(train_pos_index))node_index = np.concatenate([train_neg_index, train_pos_index])node_index.sort()pos_prototype = torch.mean(train_h[train_pos], dim=0).view(1, -1)neg_prototype = torch.mean(train_h[train_neg], dim=0).view(1, -1)train_h_loss = train_h[node_index]pos_prototypes = pos_prototype.expand(train_h_loss.shape)neg_prototypes = neg_prototype.expand(train_h_loss.shape)diff_pos = -F.pairwise_distance(train_h_loss, pos_prototypes)diff_neg = -F.pairwise_distance(train_h_loss, neg_prototypes)diff_pos = diff_pos.view(-1, 1)diff_neg = diff_neg.view(-1, 1)diff = torch.cat([diff_neg, diff_pos], dim=1)diff_loss = F.cross_entropy(diff, train_label[node_index])return agg_h, edge_diff_loss, diff_lossdef score_edges(self, edges):src = edges.src['feat']dst = edges.dst['feat']score = self.relation_aware(src, dst)return {'score': score}

4. H2FDetector 类

这是一个多层的 GNN 模型，用于构建一个关系感知的图神经网络模型。

• 多层的关系感知图神经网络模型，包含前向传播和损失计算方法。

class H2FDetector(nn.Module):def __init__(self, args, g):super().__init__()self.n_layer = args.n_layerself.input_dim = g.nodes['r'].data['feature'].shape[1]self.intra_dim = args.intra_dimself.n_class = args.n_classself.gamma1 = args.gamma1self.gamma2 = args.gamma2self.n_layer = args.n_layerself.mine_layers = nn.ModuleList()if args.n_layer == 1:self.mine_layers.append(MultiRelationH2FDetectorLayer(self.input_dim, self.n_class, args.head, g, args.dropout, if_sum=True))else:self.mine_layers.append(MultiRelationH2FDetectorLayer(self.input_dim, self.intra_dim, args.head, g, args.dropout))for _ in range(1, self.n_layer - 1):self.mine_layers.append(MultiRelationH2FDetectorLayer(self.intra_dim * args.head, self.intra_dim, args.head, g, args.dropout))self.mine_layers.append(MultiRelationH2FDetectorLayer(self.intra_dim * args.head, self.n_class, args.head, g, args.dropout, if_sum=True))self.dropout = nn.Dropout(args.dropout)self.relu = nn.ReLU()def forward(self, g):feats = g.ndata['feature'].float()h = self.mine_layers[0](g, feats)if self.n_layer > 1:h = self.relu(h)h = self.dropout(h)for i in range(1, len(self.mine_layers) - 1):h = self.mine_layers[i](g, h)h = self.relu(h)h = self.dropout(h)h = self.mine_layers[-1](g, h)return hdef loss(self, g):feats = g.ndata['feature'].float()train_mask = g.ndata['train_mask'].bool()train_label = g.ndata['label'][train_mask]train_pos = train_label == 1train_neg = train_label == 0pos_index = train_pos.nonzero().flatten().detach().cpu().numpy()neg_index = train_neg.nonzero().flatten().detach().cpu().numpy()neg_index = np.random.choice(neg_index, size=len(pos_index), replace=False)index = np.concatenate([pos_index, neg_index])index.sort()h, edge_loss, prototype_loss = self.mine_layers[0].loss(g, feats)if self.n_layer > 1:h = self.relu(h)h = self.dropout(h)for i in range(1, len(self.mine_layers) - 1):h, e_loss, p_loss = self.mine_layers[i].loss(g, h)h = self.relu(h)h = self.dropout(h)edge_loss += e_lossprototype_loss += p_lossh, e_loss, p_loss = self.mine_layers[-1].loss(g, h)edge_loss += e_lossprototype_loss += p_lossmodel_loss = F.cross_entropy(h[train_mask][index], train_label[index])loss = model_loss + self.gamma1 * edge_loss + self.gamma2 * prototype_lossreturn loss