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Hi, thanks for open source the code! The framework can learn and generate undirected graph with high quality. But when I train the model (modified based on the suggestion given in the appendix of the paper) with directed graph, I find it cannot generate similar amount of reciprocal edges compare to the training graph I have.
Specifically, The modification I have done is double the length of edge sequence of node i as (A_1i, A_i1, A_2i, A_i2 ...), where A is the adj matrix. I do know this leads to a sparser sequence.
The graph I'm training has around 200 nodes, 659 one sided directed edges and 58 reciprocal edges, while the generated graph in average has less than 520 directed edges and 15 to 20 reciprocal edges. So the model is generating sparser graph.
I wonder if anyone has experience using this framework with directed graph, and give any advise on dealing with my issue?
Thanks in advance.
The text was updated successfully, but these errors were encountered:
Hi, thanks for open source the code! The framework can learn and generate undirected graph with high quality. But when I train the model (modified based on the suggestion given in the appendix of the paper) with directed graph, I find it cannot generate similar amount of reciprocal edges compare to the training graph I have.
Specifically, The modification I have done is double the length of edge sequence of node i as (A_1i, A_i1, A_2i, A_i2 ...), where A is the adj matrix. I do know this leads to a sparser sequence.
The graph I'm training has around 200 nodes, 659 one sided directed edges and 58 reciprocal edges, while the generated graph in average has less than 520 directed edges and 15 to 20 reciprocal edges. So the model is generating sparser graph.
I wonder if anyone has experience using this framework with directed graph, and give any advise on dealing with my issue?
Thanks in advance.
The text was updated successfully, but these errors were encountered: