1. In the Introduction, the authors assert that "i) To the best of our knowledge, we are the first to learn node embeddings using the abstention-based GAT architecture." This claim seems overstated.
2. In Section 3.1, the authors introduce NodeCwR-Cov and mention that "There are two more fully connected layers after the softmax layer (with 512 nodes and one node) to model the selection function g." The meaning of "having 512 nodes and one node" is unclear in this context.
Additionally, the selection function threshold is set to 0.5, but the rationale behind choosing this value and its impact on the model or performance is not explained.
This threshold serves to filter eligible candidates. It is essential to consider the accuracy of these candidates for each threshold, as they significantly impact the overall performance.
3. The presentation of results in tables and figures is unclear. For instance, in Table 1, the meanings of Cov and LS are not explained.
The experimental analysis lacks depth and clarity.
4. GAT is chosen as the backbone for the proposed model. How does it compare to other graph neural network models?
5. In my opinion, the contribution of this paper appears somewhat limited, and the proposed model seems incremental in its approach.
5. In my opinion, the contribution of this paper appears somewhat limited, and the proposed model seems incremental in its approach.