超图实现具有不同超边权重的超图卷积模型

[wpx-1]

您好，我想请问下。在dhg文档中，有份示例代码，是用于构造具有不同超边权重的超图卷积模型（代码附在下面）。我想问的是，这个超边特征Y是不是得先进将顶点信息传递到边后，即先调用 hg.v2e才可以拿到这个超边特征。
class HGATConv(nn.Module):
def init(
self,
in_channels: int,
out_channels: int,
bias: bool = True,
drop_rate: float = 0.5,
atten_neg_slope: float = 0.2,
):
super().init()
self.atten_dropout = nn.Dropout(drop_rate)
self.atten_act = nn.LeakyReLU(atten_neg_slope)
self.act = nn.ELU(inplace=True)
self.theta_vertex = nn.Linear(in_channels, out_channels, bias=bias)
self.theta_hyperedge = nn.Linear(in_channels, out_channels, bias=bias)
self.atten_vertex = nn.Linear(out_channels, 1, bias=False)
self.atten_hyperedge = nn.Linear(out_channels, 1, bias=False)

def forward(self, X: torch.Tensor, Y: torch.Tensor, hg: dhg.Hypergraph) -> torch.Tensor:
    X = self.theta_vertex(X)
    Y = self.theta_hyperedge(Y)
    x_for_vertex = self.atten_vertex(X)
    y_for_hyperedge = self.atten_hyperedge(Y)
    v2e_atten_score = x_for_vertex[hg.v2e_src] + y_for_hyperedge[hg.v2e_dst]
    e2v_atten_score = y_for_hyperedge[hg.e2v_src] + x_for_vertex[hg.e2v_dst]
    v2e_atten_score = self.atten_dropout(self.atten_act(v2e_atten_score).squeeze())
    e2v_atten_score = self.atten_dropout(self.atten_act(e2v_atten_score).squeeze())
    Y_ = hg.v2e(X, aggr="softmax_then_sum", v2e_weight=v2e_atten_score)
    X_ = hg.e2v(Y_, aggr="softmax_then_sum", e2v_weight=e2v_atten_score)
    X_ = self.act(X_)
    Y_ = self.act(Y_)
    return X_