import dgl
import torch
import torch.nn as nn
import dgl.function as Fn
import torch.nn.functional as F
from dgl.ops import edge_softmax
from dgl.nn.pytorch import TypedLinear
from ..utils import to_hetero_feat
from . import BaseModel, register_model
[docs]@register_model('SimpleHGN')
class SimpleHGN(BaseModel):
r"""
This is a model SimpleHGN from `Are we really making much progress? Revisiting, benchmarking, and
refining heterogeneous graph neural networks
<https://dl.acm.org/doi/pdf/10.1145/3447548.3467350>`__
The model extend the original graph attention mechanism in GAT by including edge type information into attention calculation.
Calculating the coefficient:
.. math::
\alpha_{ij} = \frac{exp(LeakyReLU(a^T[Wh_i||Wh_j||W_r r_{\psi(<i,j>)}]))}{\Sigma_{k\in\mathcal{E}}{exp(LeakyReLU(a^T[Wh_i||Wh_k||W_r r_{\psi(<i,k>)}]))}} \quad (1)
Residual connection including Node residual:
.. math::
h_i^{(l)} = \sigma(\Sigma_{j\in \mathcal{N}_i} {\alpha_{ij}^{(l)}W^{(l)}h_j^{(l-1)}} + h_i^{(l-1)}) \quad (2)
and Edge residual:
.. math::
\alpha_{ij}^{(l)} = (1-\beta)\alpha_{ij}^{(l)}+\beta\alpha_{ij}^{(l-1)} \quad (3)
Multi-heads:
.. math::
h^{(l+1)}_j = \parallel^M_{m = 1}h^{(l + 1, m)}_j \quad (4)
Residual:
.. math::
h^{(l+1)}_j = h^{(l)}_j + \parallel^M_{m = 1}h^{(l + 1, m)}_j \quad (5)
Parameters
----------
edge_dim: int
the edge dimension
num_etypes: int
the number of the edge type
in_dim: int
the input dimension
hidden_dim: int
the output dimension
num_classes: int
the number of the output classes
num_layers: int
the number of layers we used in the computing
heads: list
the list of the number of heads in each layer
feat_drop: float
the feature drop rate
negative_slope: float
the negative slope used in the LeakyReLU
residual: boolean
if we need the residual operation
beta: float
the hyperparameter used in edge residual
"""
[docs] @classmethod
def build_model_from_args(cls, args, hg):
heads = [args.num_heads] * args.num_layers + [1]
return cls(args.edge_dim,
len(hg.etypes),
[args.hidden_dim],
args.hidden_dim // args.num_heads,
args.out_dim,
args.num_layers,
heads,
args.feats_drop_rate,
args.slope,
True,
args.beta,
hg.ntypes
)
def __init__(self, edge_dim, num_etypes, in_dim, hidden_dim, num_classes,
num_layers, heads, feat_drop, negative_slope,
residual, beta, ntypes):
super(SimpleHGN, self).__init__()
self.ntypes = ntypes
self.num_layers = num_layers
self.hgn_layers = nn.ModuleList()
self.activation = F.elu
# input projection (no residual)
self.hgn_layers.append(
SimpleHGNConv(
edge_dim,
in_dim[0],
hidden_dim,
heads[0],
num_etypes,
feat_drop,
negative_slope,
False,
self.activation,
beta=beta,
)
)
# hidden layers
for l in range(1, num_layers - 1): # noqa E741
# due to multi-head, the in_dim = hidden_dim * num_heads
self.hgn_layers.append(
SimpleHGNConv(
edge_dim,
hidden_dim * heads[l - 1],
hidden_dim,
heads[l],
num_etypes,
feat_drop,
negative_slope,
residual,
self.activation,
beta=beta,
)
)
# output projection
self.hgn_layers.append(
SimpleHGNConv(
edge_dim,
hidden_dim * heads[-2],
num_classes,
heads[-1],
num_etypes,
feat_drop,
negative_slope,
residual,
None,
beta=beta,
)
)
[docs] def forward(self, hg, h_dict):
"""
The forward part of the SimpleHGN.
Parameters
----------
hg : object
the dgl heterogeneous graph
h_dict: dict
the feature dict of different node types
Returns
-------
dict
The embeddings after the output projection.
"""
if hasattr(hg, 'ntypes'):
# full graph training,
with hg.local_scope():
hg.ndata['h'] = h_dict
g = dgl.to_homogeneous(hg, ndata = 'h')
h = g.ndata['h']
for l in range(self.num_layers): # noqa E741
h = self.hgn_layers[l](g, h, g.ndata['_TYPE'], g.edata['_TYPE'], True)
h = h.flatten(1)
h_dict = to_hetero_feat(h, g.ndata['_TYPE'], hg.ntypes)
else:
# for minibatch training, input h_dict is a tensor
h = h_dict
for layer, block in zip(self.hgn_layers, hg):
h = layer(block, h, block.ndata['_TYPE']['_N'], block.edata['_TYPE'], presorted=False)
h_dict = to_hetero_feat(h, block.ndata['_TYPE']['_N'][:block.num_dst_nodes()], self.ntypes)
return h_dict
@property
def to_homo_flag(self):
return True
class SimpleHGNConv(nn.Module):
r"""
The SimpleHGN convolution layer.
Parameters
----------
edge_dim: int
the edge dimension
num_etypes: int
the number of the edge type
in_dim: int
the input dimension
out_dim: int
the output dimension
num_heads: int
the number of heads
num_etypes: int
the number of edge type
feat_drop: float
the feature drop rate
negative_slope: float
the negative slope used in the LeakyReLU
residual: boolean
if we need the residual operation
activation: str
the activation function
beta: float
the hyperparameter used in edge residual
"""
def __init__(self, edge_dim, in_dim, out_dim, num_heads, num_etypes, feat_drop=0.0,
negative_slope=0.2, residual=True, activation=F.elu, beta=0.0):
super(SimpleHGNConv, self).__init__()
self.edge_dim = edge_dim
self.in_dim = in_dim
self.out_dim = out_dim
self.num_heads = num_heads
self.num_etypes = num_etypes
self.edge_emb = nn.Parameter(torch.empty(size=(num_etypes, edge_dim)))
self.W = nn.Parameter(torch.FloatTensor(
in_dim, out_dim * num_heads))
self.W_r = TypedLinear(edge_dim, edge_dim * num_heads, num_etypes)
self.a_l = nn.Parameter(torch.empty(size=(1, num_heads, out_dim)))
self.a_r = nn.Parameter(torch.empty(size=(1, num_heads, out_dim)))
self.a_e = nn.Parameter(torch.empty(size=(1, num_heads, edge_dim)))
nn.init.xavier_uniform_(self.edge_emb, gain=1.414)
nn.init.xavier_uniform_(self.W, gain=1.414)
nn.init.xavier_uniform_(self.a_l.data, gain=1.414)
nn.init.xavier_uniform_(self.a_r.data, gain=1.414)
nn.init.xavier_uniform_(self.a_e.data, gain=1.414)
self.feat_drop = nn.Dropout(feat_drop)
self.leakyrelu = nn.LeakyReLU(negative_slope)
self.activation = activation
if residual:
self.residual = nn.Linear(in_dim, out_dim * num_heads)
else:
self.register_buffer("residual", None)
self.beta = beta
def forward(self, g, h, ntype, etype, presorted = False):
"""
The forward part of the SimpleHGNConv.
Parameters
----------
g : object
the dgl homogeneous graph
h: tensor
the original features of the graph
ntype: tensor
the node type of the graph
etype: tensor
the edge type of the graph
presorted: boolean
if the ntype and etype are preordered, default: ``False``
Returns
-------
tensor
The embeddings after aggregation.
"""
emb = self.feat_drop(h)
emb = torch.matmul(emb, self.W).view(-1, self.num_heads, self.out_dim)
emb[torch.isnan(emb)] = 0.0
edge_emb = self.W_r(self.edge_emb[etype], etype, presorted).view(-1,
self.num_heads, self.edge_dim)
row = g.edges()[0]
col = g.edges()[1]
h_l = (self.a_l * emb).sum(dim=-1)[row]
h_r = (self.a_r * emb).sum(dim=-1)[col]
h_e = (self.a_e * edge_emb).sum(dim=-1)
edge_attention = self.leakyrelu(h_l + h_r + h_e)
edge_attention = edge_softmax(g, edge_attention)
if 'alpha' in g.edata.keys():
res_attn = g.edata['alpha']
edge_attention = edge_attention * \
(1 - self.beta) + res_attn * self.beta
if self.num_heads == 1:
edge_attention = edge_attention[:, 0]
edge_attention = edge_attention.unsqueeze(1)
with g.local_scope():
emb = emb.permute(0, 2, 1).contiguous()
g.edata['alpha'] = edge_attention
g.srcdata['emb'] = emb
g.update_all(Fn.u_mul_e('emb', 'alpha', 'm'),
Fn.sum('m', 'emb'))
h_output = g.dstdata['emb'].view(-1, self.out_dim * self.num_heads)
# h_prime = []
# for i in range(self.num_heads):
# g.edata['alpha'] = edge_attention[:, i]
# g.srcdata.update({'emb': emb[i]})
# g.update_all(Fn.u_mul_e('emb', 'alpha', 'm'),
# Fn.sum('m', 'emb'))
# h_prime.append(g.ndata['emb'])
# h_output = torch.cat(h_prime, dim=1)
g.edata['alpha'] = edge_attention
if g.is_block:
h = h[:g.num_dst_nodes()]
if self.residual:
res = self.residual(h)
h_output += res
if self.activation is not None:
h_output = self.activation(h_output)
return h_output