def forward(
self,
graph: Graph,
x: torch.Tensor,
):
graph.sym_norm()
return self.encoder(graph, x)
def forward(
self,
graph: Graph,
x: torch.Tensor = None,
):
if x is None:
x = graph.x
graph.sym_norm()
return self.encoder(graph, x)
def _add_undirected_graph_positional_embedding(g: Graph,
hidden_size,
retry=10):
# We use eigenvectors of normalized graph laplacian as vertex features.
# It could be viewed as a generalization of positional embedding in the
# attention is all you need paper.
# Recall that the eignvectors of normalized laplacian of a line graph are cos/sin functions.
# See section 2.4 of http://www.cs.yale.edu/homes/spielman/561/2009/lect02-09.pdf
n = g.num_nodes
with g.local_graph():
g.sym_norm()
adj = g.to_scipy_csr()
laplacian = adj
k = min(n - 2, hidden_size)
x = eigen_decomposision(n, k, laplacian, hidden_size, retry)
g.pos_undirected = x.float()
return g
