def forward(self, g, h, e):
h_in = h # for residual connection
e_in = e # for residual connection
g.ndata['h'] = h
g.ndata['Ah'] = self.A(h)
g.ndata['Bh'] = self.B(h)
g.ndata['Dh'] = self.D(h)
g.ndata['Eh'] = self.E(h)
g.edata['e'] = e
g.edata['Ce'] = self.C(e)
g.update_all(self.message_func, self.reduce_func)
h = g.ndata['h'] # result of graph convolution
e = g.edata['e'] # result of graph convolution
if self.norm is not None:
normalize(self.bn_node_h, h, g)
normalize(self.bn_node_e, e, g)
h = F.relu(h) # non-linear activation
e = F.relu(e) # non-linear activation
if self.residual:
h = h_in + h # residual connection
e = e_in + e # residual connection
h = F.dropout(h, self.dropout, training=self.training)
e = F.dropout(e, self.dropout, training=self.training)
return h, e
def forward(self, g, h):
h_in = h # for residual connection
h = self.gatconv(g, h).flatten(1)
if self.norm is not None:
normalize(self.batchnorm_h, h, g)
if self.activation:
h = self.activation(h)
if self.residual:
h = h_in + h # residual connection
return h
def forward(self, g, feature):
h_in = feature # to be used for residual connection
if self.dgl_builtin == False:
g.ndata['h'] = feature
g.update_all(msg, reduce)
g.apply_nodes(func=self.apply_mod)
h = g.ndata['h'] # result of graph convolution
else:
h = self.conv(g, feature)
if self.norm is not None:
normalize(self.batchnorm_h, h, g)
if self.activation:
h = self.activation(h)
if self.residual:
h = h_in + h # residual connection
h = self.dropout(h)
return h
def forward(self, g, h):
z = self.fc(h)
g.ndata['z'] = z
g.update_all(self.message_func, self.reduce_func)
h = g.ndata['h']
if self.norm is not None:
h = normalize(self.batchnorm_h, h, g)
h = F.elu(h)
h = F.dropout(h, self.dropout, training=self.training)
return h
def forward(self, g, h, e):
z_h = self.fc_h(h)
z_e = self.fc_e(e)
g.ndata['z_h'] = z_h
g.edata['z_e'] = z_e
g.apply_edges(self.edge_attention)
g.update_all(self.message_func, self.reduce_func)
h = g.ndata['h']
e = g.edata['e_proj']
if self.norm is not None:
h = normalize(self.batchnorm_h, h, g)
e = normalize(self.batchnorm_e, e, g)
h = F.elu(h)
e = F.elu(e)
h = F.dropout(h, self.dropout, training=self.training)
e = F.dropout(e, self.dropout, training=self.training)
return h, e