1.The problem formulation is somewhat unclear in the statement and introduction examples. 2. More baselines or self variants should be compared to better prove the effectiveness.
Detailed comments:
The problem definition of keyword search on incomplete graphs is ambiguous and confusing. The KS-GNN mostly optimizes on node similarity and the inference stage tends to select the top-k most similar results towards the query keyword set. However, the problem itself seems more like a combinatorial one, or say, set optimization, node-set selection with minimal distance measurement. Are the targets here equivalent?
The baseline approach seems much inferior to KS-GNN. It would be great to include some variants of the KS-GNN that delete some of the module or training objectives to confirm the contribution of each component.
Table 2 with missing edges is supposed to be more challenging than the task in Table 1. However, a lot of models perform even better (or comparable) which seems strange. Also, the claim of “KS-GNN has no significant effect” does not apply to the Toy and Video datasets for correctness.
It is hard to conclude from Figure 4 on the benefit of keyword frequency regularization. That is, it’s better to show the performance scores along with the visualization.
Figure 3: The notations of the figure (especially function f and g) are confusing.
1.The problem formulation is somewhat unclear in the statement and introduction examples.