[Question] --has_edge_importance

[ccvalley]

When using the --has_edge_importance argument in the dglke_train function, is a higher edge importance score weighted as more important than a lower edge importance score? Or vice versa?

Based on the get_total_loss function, it seems that a lower edge weight would be more favorable in the loss calculation.

dgl-ke/python/dglke/models/pytorch/loss.py

Lines 69 to 77 in b4e5701

	def get_total_loss(self, pos_score, neg_score, edge_weight=None):
	log = {}
	if edge_weight is None:
	edge_weight = 1
	if self.pairwise:
	pos_score = pos_score.unsqueeze(-1)
	loss = th.mean(self.loss_criterion((pos_score - neg_score), 1) * edge_weight)
	log['loss'] = get_scalar(loss)
	return loss, log

Thank you.

    def get_total_loss(self, pos_score, neg_score, edge_weight=None):
        log = {}
        if edge_weight is None:
            edge_weight = 1
        if self.pairwise:
            pos_score = pos_score.unsqueeze(-1)
            loss = th.mean(self.loss_criterion((pos_score - neg_score), 1) * edge_weight)
            log['loss'] = get_scalar(loss)
            return loss, log

[classicsong]

The higher edge_weight, it means it will contribute more to the loss.
Usually you can assign higher weight to low frequency relations, to handle the data imbalance problem.