yueliu1999 / dcrn Goto Github PK

你好，我想请问一下，跑10次取平均，这10次的参数设置有什么不一样吗，是随机种子的设置不一样吗，如果是的话，那具体取什么值

Congratulations to your paper, great work! I have some issues for your help. How can I can other datasets reported in your paper, like CITE, ACM, etc. Besides, how can I run the algorithm on my own datasets?

Hello, I am also doing research on graph network now. Your article is very enlightening. I would like to ask, when I was looking for data sets, I found that data sets like CITEand DBLP are all in text or json format, and I could not find dataset with adjacency matrix and attribute data. I want to know whether your experimental data is processed by yourself and then experimented with these original data sets, or where you can find the data in graph format?

您好，請問使用batch-DCRN進行訓練時顯示
size mismatch for b: copying a param with shape torch.Size([10000, 20]) from checkpoint, the shape in current model is torch.Size([1]).
請問該怎麼解決呢？

你好，请问一下，预训练的代码在哪里呢？

你好，可以提供一下创建邻接矩阵的代码吗？

在`def model_init(model, X, y, A_norm):
"""
load the pre-train model and calculate similarity and cluster centers
Args:
model: Dual Correlation Reduction Network
X: input feature matrix
y: input label
A_norm: normalized adj
Returns: embedding similarity matrix
"""
# load pre-train model
model = load_pretrain_parameter(model)

# calculate embedding similarity
with torch.no_grad():
    _, _, _, sim, _, _, _, Z, _, _ = model(X, A_norm, X, A_norm)

# calculate cluster centers
acc, nmi, ari, f1, centers = clustering(Z, y)

return sim, centers`中
    _, _, _, sim, _, _, _, Z, _, _ = model(X, A_norm, X, A_norm)中X,A_norm出现了两次

在传入forward(self, X_tilde1, Am, X_tilde2, Ad)函数时是应该是x1一波和x2一波，

这样的话取平均好像好不取平均是一样的了 Z_ae = (Z_ae1 + Z_ae2) / 2
Z_igae = (Z_igae1 + Z_igae2) / 2

这个地方不太理解，可以解答一下吗？

您好！請問一下DCRN可以用在類似於OFFICE數據集等無監督圖想聚類任務嗎？如果要應用，怎麼生成符合要求的.npy文件呢？

您好，请问能权重的预训练代码在哪里呢 项目里没找到？

您好，我根据您给出的预训练代码进行预训练，将该预训练参数文件放到模型中训练，acm和cite得到的acc在论文的acc范围内，但dblp的acc最好也才是77.42，始终比论文中的要低2，可是所有的预训练步骤都一样，该改的超参数也会相应的修改，这样可能是出了什么问题呢？