flyingtango/digcn

Implement of DiGCN, NeurIPS-2020

digcn's Issues

Can be applied to large-scale graph?

I think that this work cannot be applied to large-scale graphs for the reason that calculating the adj through your method needs eigen decomposition and to_dense() method needs large memory available.
eig_value, left_vector = scipy.linalg.eig(p_ppr.numpy(),left=True,right=False) p_dense = torch.sparse.FloatTensor(edge_index, p, torch.Size([num_nodes,num_nodes])).to_dense()

Differences in results between DiGCN and DGCN

Hi,

I was wondering, what is the large discrepancy in accuracy for benchmarking methods (like GCN/GAT, for example) between Table 1 in the DGCN paper and Table 2 in the DiGCN paper? The accuracy levels seem very different for the same dataset.

Is this work extendable using other graph datasets like Pubmed or Reddit-binary?

Dear author,

I have collected my graphs with different format like PubMed/ Reddit-Binary. if your work is extendable, may I know which parts of code I need to change? Thank you in advance,

Question about equation 4

Dear author,
I'm a little confused about Eq.4. You adopt symmetric normalized directed Laplacian proposed by Chung F (2005), however, It is not clear to me why the graph filter in Eq 4 can reflect directed property? Since it is a symmetric matrix.

您好！请问您在链路预测的时候是如何对有向图进行链路预测的呢？

研一小菜鸡被老板要求做有向图的链路预测，像GAE那样两个节点的嵌入做内积的话还原的是无向图，没什么思路很苦恼，正好看见了您的文章，所以就想问一下您是怎么处理有向图的链路预测的？