xdweixia / sgc-pld Goto Github PK

在Graph_Attention_Encoder.py中，第34行：
self.z = self.__dense_layer(self.H)
self.XZ = self._dense_layer_Z(self.H)
输入怎么都是self.H, 自己和自己进行度量？

您好，我尝试按照论文中的描述，调整模型的参数值：

对应到代码中这一部分：

# Total lossA
self.loss = (features_loss + self.lambda_ * structure_loss) + 0.1 * self.XZ_loss + 5 * self.C_loss  +  1 *self.dense_loss
# self.loss = features_loss + self.lambda_ * structure_loss + 10 * self.C_loss + self.dense_loss # without pd 0.678
# self.loss = features_loss + self.lambda_ * structure_loss + 10 * self.C_loss + 100 * self.XZ_loss # without ss 0.64
# self.loss = features_loss + self.lambda_ * structure_loss  + 100 * self.XZ_loss + self.dense_loss # without clustering loss
return self.loss, self.H, self.C, self.pred, self.dense_loss, self.z, features_loss, structure_loss#, self.C_loss, self.XZ_loss

最终在Cora数据集上得到如下结果：

Epoch--995: time: 501.838:
acc: 0.67872969 nmi: 0.51988349 f_score: 0.60493104 ari: 0.45633046
Epoch--996: time: 502.479:
acc: 0.67909897 nmi: 0.51989684 f_score: 0.60486178 ari: 0.45696338
Epoch--997: time: 503.102:
acc: 0.67836041 nmi: 0.51759465 f_score: 0.60393833 ari: 0.45571426
Epoch--998: time: 503.696:
acc: 0.67651403 nmi: 0.51489665 f_score: 0.60190142 ari: 0.45244131
Epoch--999: time: 504.392:
acc: 0.67762186 nmi: 0.51728817 f_score: 0.60408646 ari: 0.45316671

而预期的准确率能达到 0.76。请问我是否还应该修改代码的其他部分？另外如果可以的话，请您提供一下Citesser 和 Pubmed 的参数配置！非常感谢！