I mainly have the following concerns. 1) In general, this paper is incremental to GIN [1], which limits the contribution of this paper. While GIN is well motivated by WL test with solid theoretical background, this paper lacks deeper analysis and new motivation behind the algorithm design. I suggest the authors to give more insightful analysis and motivation. 2) I noticed that in Sec 5.3, a generator equipped with a standard R-GCN as discriminator tends to collapse after several (around 20), while the proposed module will not. The reason behind this fact can be essential to show the mechanism how the proposed method differs from previous one. However, this part is missing in this version of submission. I would like to see why the proposed module can prevent a generator from collapsing. 3) I understand that stochastic/random projection is with high probability to preserve the metric before mapping . My concern is that when stacking multiple layers of WLS units, the probability of the failure case of stochastic/random projection also increases (since projection is performed at each layer). This may greatly hinder the scenario of the proposed method from forming deeper GNN. In this case, authors should justify the stability of the proposed method. How stable is the proposed method? And what happens when stacking more layers?
2) I noticed that in Sec 5.3, a generator equipped with a standard R-GCN as discriminator tends to collapse after several (around 20), while the proposed module will not. The reason behind this fact can be essential to show the mechanism how the proposed method differs from previous one. However, this part is missing in this version of submission. I would like to see why the proposed module can prevent a generator from collapsing.