270 lines
11 KiB
Python
270 lines
11 KiB
Python
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import dgl.function as fn
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from dgl.nn import GraphConv
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from dgl.ops import edge_softmax
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class HeCoGATConv(nn.Module):
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def __init__(self, hidden_dim, attn_drop=0.0, negative_slope=0.01, activation=None):
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"""HeCo作者代码中使用的GAT
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:param hidden_dim: int 隐含特征维数
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:param attn_drop: float 注意力dropout
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:param negative_slope: float, optional LeakyReLU负斜率，默认为0.01
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:param activation: callable, optional 激活函数，默认为None
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"""
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super().__init__()
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self.attn_l = nn.Parameter(torch.FloatTensor(1, hidden_dim))
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self.attn_r = nn.Parameter(torch.FloatTensor(1, hidden_dim))
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self.attn_drop = nn.Dropout(attn_drop)
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self.leaky_relu = nn.LeakyReLU(negative_slope)
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self.activation = activation
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self.reset_parameters()
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def reset_parameters(self):
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gain = nn.init.calculate_gain('relu')
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nn.init.xavier_normal_(self.attn_l, gain)
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nn.init.xavier_normal_(self.attn_r, gain)
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def forward(self, g, feat_src, feat_dst):
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"""
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:param g: DGLGraph 邻居-目标顶点二分图
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:param feat_src: tensor(N_src, d) 邻居顶点输入特征
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:param feat_dst: tensor(N_dst, d) 目标顶点输入特征
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:return: tensor(N_dst, d) 目标顶点输出特征
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"""
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with g.local_scope():
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# HeCo作者代码中使用attn_drop的方式与原始GAT不同，这样是不对的，却能顶点聚类提升性能……
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attn_l = self.attn_drop(self.attn_l)
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attn_r = self.attn_drop(self.attn_r)
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el = (feat_src * attn_l).sum(dim=-1).unsqueeze(dim=-1) # (N_src, 1)
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er = (feat_dst * attn_r).sum(dim=-1).unsqueeze(dim=-1) # (N_dst, 1)
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g.srcdata.update({'ft': feat_src, 'el': el})
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g.dstdata['er'] = er
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g.apply_edges(fn.u_add_v('el', 'er', 'e'))
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e = self.leaky_relu(g.edata.pop('e'))
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g.edata['a'] = edge_softmax(g, e) # (E, 1)
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# 消息传递
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g.update_all(fn.u_mul_e('ft', 'a', 'm'), fn.sum('m', 'ft'))
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ret = g.dstdata['ft']
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if self.activation:
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ret = self.activation(ret)
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return ret
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class Attention(nn.Module):
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def __init__(self, hidden_dim, attn_drop):
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"""语义层次的注意力
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:param hidden_dim: int 隐含特征维数
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:param attn_drop: float 注意力dropout
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"""
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super().__init__()
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self.fc = nn.Linear(hidden_dim, hidden_dim)
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self.attn = nn.Parameter(torch.FloatTensor(1, hidden_dim))
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self.attn_drop = nn.Dropout(attn_drop)
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self.reset_parameters()
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def reset_parameters(self):
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gain = nn.init.calculate_gain('relu')
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nn.init.xavier_normal_(self.fc.weight, gain)
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nn.init.xavier_normal_(self.attn, gain)
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def forward(self, h):
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"""
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:param h: tensor(N, M, d) 顶点基于不同元路径/类型的嵌入，N为顶点数，M为元路径/类型数
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:return: tensor(N, d) 顶点的最终嵌入
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"""
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attn = self.attn_drop(self.attn)
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# (N, M, d) -> (M, d) -> (M, 1)
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w = torch.tanh(self.fc(h)).mean(dim=0).matmul(attn.t())
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beta = torch.softmax(w, dim=0) # (M, 1)
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beta = beta.expand((h.shape[0],) + beta.shape) # (N, M, 1)
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z = (beta * h).sum(dim=1) # (N, d)
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return z
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class NetworkSchemaEncoder(nn.Module):
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def __init__(self, hidden_dim, attn_drop, relations):
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"""网络结构视图编码器
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:param hidden_dim: int 隐含特征维数
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:param attn_drop: float 注意力dropout
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:param relations: List[(str, str, str)] 目标顶点关联的关系列表，长度为邻居类型数S
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"""
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super().__init__()
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self.relations = relations
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self.dtype = relations[0][2]
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self.gats = nn.ModuleDict({
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r[0]: HeCoGATConv(hidden_dim, attn_drop, activation=F.elu)
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for r in relations
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})
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self.attn = Attention(hidden_dim, attn_drop)
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def forward(self, g, feats):
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"""
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:param g: DGLGraph 异构图
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:param feats: Dict[str, tensor(N_i, d)] 顶点类型到输入特征的映射
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:return: tensor(N_dst, d) 目标顶点的最终嵌入
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"""
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feat_dst = feats[self.dtype][:g.num_dst_nodes(self.dtype)]
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h = []
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for stype, etype, dtype in self.relations:
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h.append(self.gats[stype](g[stype, etype, dtype], feats[stype], feat_dst))
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h = torch.stack(h, dim=1) # (N_dst, S, d)
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z_sc = self.attn(h) # (N_dst, d)
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return z_sc
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class PositiveGraphEncoder(nn.Module):
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def __init__(self, num_metapaths, in_dim, hidden_dim, attn_drop):
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"""正样本视图编码器
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:param num_metapaths: int 元路径数量M
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:param hidden_dim: int 隐含特征维数
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:param attn_drop: float 注意力dropout
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"""
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super().__init__()
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self.gcns = nn.ModuleList([
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GraphConv(in_dim, hidden_dim, norm='right', activation=nn.PReLU())
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for _ in range(num_metapaths)
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])
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self.attn = Attention(hidden_dim, attn_drop)
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def forward(self, mgs, feats):
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"""
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:param mgs: List[DGLGraph] 正样本图
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:param feats: List[tensor(N, d)] 输入顶点特征
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:return: tensor(N, d) 输出顶点特征
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"""
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h = [gcn(mg, feat) for gcn, mg, feat in zip(self.gcns, mgs, feats)]
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h = torch.stack(h, dim=1) # (N, M, d)
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z_pg = self.attn(h) # (N, d)
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return z_pg
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class Contrast(nn.Module):
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def __init__(self, hidden_dim, tau, lambda_):
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"""对比损失模块
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:param hidden_dim: int 隐含特征维数
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:param tau: float 温度参数
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:param lambda_: float 0~1之间，网络结构视图损失的系数（元路径视图损失的系数为1-λ）
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"""
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super().__init__()
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self.proj = nn.Sequential(
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nn.Linear(hidden_dim, hidden_dim),
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nn.ELU(),
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nn.Linear(hidden_dim, hidden_dim)
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)
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self.tau = tau
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self.lambda_ = lambda_
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self.reset_parameters()
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def reset_parameters(self):
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gain = nn.init.calculate_gain('relu')
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for model in self.proj:
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if isinstance(model, nn.Linear):
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nn.init.xavier_normal_(model.weight, gain)
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def sim(self, x, y):
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"""计算相似度矩阵
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:param x: tensor(N, d)
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:param y: tensor(N, d)
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:return: tensor(N, N) S[i, j] = exp(cos(x[i], y[j]))
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"""
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x_norm = torch.norm(x, dim=1, keepdim=True)
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y_norm = torch.norm(y, dim=1, keepdim=True)
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numerator = torch.mm(x, y.t())
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denominator = torch.mm(x_norm, y_norm.t())
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return torch.exp(numerator / denominator / self.tau)
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def forward(self, z_sc, z_mp, pos):
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"""
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:param z_sc: tensor(N, d) 目标顶点在网络结构视图下的嵌入
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:param z_mp: tensor(N, d) 目标顶点在元路径视图下的嵌入
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:param pos: tensor(B, N) 0-1张量，每个目标顶点的正样本
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（B是batch大小，真正的目标顶点；N是B个目标顶点加上其正样本后的顶点数）
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:return: float 对比损失
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"""
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z_sc_proj = self.proj(z_sc)
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z_mp_proj = self.proj(z_mp)
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sim_sc2mp = self.sim(z_sc_proj, z_mp_proj)
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sim_mp2sc = sim_sc2mp.t()
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batch = pos.shape[0]
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sim_sc2mp = sim_sc2mp / (sim_sc2mp.sum(dim=1, keepdim=True) + 1e-8) # 不能改成/=
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loss_sc = -torch.log(torch.sum(sim_sc2mp[:batch] * pos, dim=1)).mean()
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sim_mp2sc = sim_mp2sc / (sim_mp2sc.sum(dim=1, keepdim=True) + 1e-8)
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loss_mp = -torch.log(torch.sum(sim_mp2sc[:batch] * pos, dim=1)).mean()
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return self.lambda_ * loss_sc + (1 - self.lambda_) * loss_mp
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class HeCo(nn.Module):
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def __init__(self, in_dims, hidden_dim, feat_drop, attn_drop, relations, tau, lambda_):
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"""HeCo模型
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:param in_dims: Dict[str, int] 顶点类型到输入特征维数的映射
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:param hidden_dim: int 隐含特征维数
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:param feat_drop: float 输入特征dropout
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:param attn_drop: float 注意力dropout
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:param relations: List[(str, str, str)] 目标顶点关联的关系列表，长度为邻居类型数S
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:param tau: float 温度参数
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:param lambda_: float 0~1之间，网络结构视图损失的系数（元路径视图损失的系数为1-λ）
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"""
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super().__init__()
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self.dtype = relations[0][2]
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self.fcs = nn.ModuleDict({
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ntype: nn.Linear(in_dim, hidden_dim) for ntype, in_dim in in_dims.items()
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})
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self.feat_drop = nn.Dropout(feat_drop)
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self.sc_encoder = NetworkSchemaEncoder(hidden_dim, attn_drop, relations)
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self.mp_encoder = PositiveGraphEncoder(len(relations), hidden_dim, hidden_dim, attn_drop)
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self.contrast = Contrast(hidden_dim, tau, lambda_)
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self.reset_parameters()
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def reset_parameters(self):
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gain = nn.init.calculate_gain('relu')
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for ntype in self.fcs:
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nn.init.xavier_normal_(self.fcs[ntype].weight, gain)
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def forward(self, g, feats, mgs, mg_feats, pos):
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"""
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:param g: DGLGraph 异构图
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:param feats: Dict[str, tensor(N_i, d_in)] 顶点类型到输入特征的映射
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:param mgs: List[DGLBlock] 正样本图，len(mgs)=元路径数量=目标顶点邻居类型数S≠模型层数
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:param mg_feats: List[tensor(N_pos_src, d_in)] 正样本图源顶点的输入特征
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:param pos: tensor(B, N) 布尔张量，每个顶点的正样本
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（B是batch大小，真正的目标顶点；N是B个目标顶点加上其正样本后的顶点数）
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:return: float, tensor(B, d_hid) 对比损失，元路径编码器输出的目标顶点特征
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"""
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h = {ntype: F.elu(self.feat_drop(self.fcs[ntype](feat))) for ntype, feat in feats.items()}
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mg_h = [F.elu(self.feat_drop(self.fcs[self.dtype](mg_feat))) for mg_feat in mg_feats]
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z_sc = self.sc_encoder(g, h) # (N, d_hid)
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z_mp = self.mp_encoder(mgs, mg_h) # (N, d_hid)
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loss = self.contrast(z_sc, z_mp, pos)
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return loss, z_mp[:pos.shape[0]]
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@torch.no_grad()
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def get_embeds(self, mgs, feats):
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"""计算目标顶点的最终嵌入(z_mp)
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:param mgs: List[DGLBlock] 正样本图
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:param feats: List[tensor(N_pos_src, d_in)] 正样本图源顶点的输入特征
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:return: tensor(N_tgt, d_hid) 目标顶点的最终嵌入
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"""
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h = [F.elu(self.fcs[self.dtype](feat)) for feat in feats]
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z_mp = self.mp_encoder(mgs, h)
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return z_mp
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