Hi, thanks your work!
I have some confusion about the code.
I want to know what self.word_context means, and why concat with x1(pair_h = torch.cat((q1, x1), dim=-1)，
q1 = self.word_context.weight[0:].view(1, -1).repeat(x1.shape[0],1).view(x1.shape[0], self.out_features))?
It doesn't seem to be reflected in the formula.
When AGGR(edge) aggregates features of hyperedges to nodes, pair_h = torch.cat((q1, y1), dim=-1) , q1 are hyperedge features, y1 are node features. So, I guess whether q1 is the hyperedge feature when nodes features aggregate to hyperedges features?
If the guess is correct? But why self.word_context = nn.Embedding(1, self.out_features), instead of self.word_context = nn.Embedding(n_hyperedge, self.out_features). Don't we need to distinguish features of hyperedges?
Hi, thanks your work!
I have some confusion about the code.
I want to know what
self.word_contextmeans, and why concat with x1(pair_h = torch.cat((q1, x1), dim=-1)，q1 = self.word_context.weight[0:].view(1, -1).repeat(x1.shape[0],1).view(x1.shape[0], self.out_features))?It doesn't seem to be reflected in the formula.
When AGGR(edge) aggregates features of hyperedges to nodes,
pair_h = torch.cat((q1, y1), dim=-1), q1 are hyperedge features, y1 are node features. So, I guess whether q1 is the hyperedge feature when nodes features aggregate to hyperedges features?If the guess is correct? But why
self.word_context = nn.Embedding(1, self.out_features), instead ofself.word_context = nn.Embedding(n_hyperedge, self.out_features). Don't we need to distinguish features of hyperedges?