1. The proposed sparsity technique seems to be limited in scope. While it has been shown to work well for a particular misinformation detector, there is no guarantee that it will work well for other networks. 2. Though the experimental results are encouraging, it is not clear why the simple pre-fixed should work well. No explanation is provided. 3. The dataset used for evaluation is not a widely used dataset. Basically only previous work has used it and this work is an extension of the previous work. 4. There is no novelty in the methodological aspects of the work.
Questions for the authors: 1. Why do misinformation detection models need to be deployed on smartphones? Can you give a real-world use case? 2. How does the proposed sparsity pattern compare with masks that are inferred from a pretrained model or learned during training? 3. Wu et al use event level detection results for document: "these two tasks are not mutually independent. Intuitively, document-level detection can benefit from the results of event-level detection, because the presence of a large number of false events indicates that the document is more likely to be fake. Therefore, we feed the results produced by a well-trained event-level detector into each layer of the document-level detector." Their ablation study (Table 4) shows that event level detection is crucial for getting best document level results (86.76 vs 84.57 F1). This seems to go against your claim that document level detection model needs to be separated from event level detection model. 4. Results in Table 1 (doc classifier exit #4) doesn't match with those of Wu et al. Why is sparse model giving better results than unpruned? 5. HSF, GROVER and CDMD are fake news detection algorithms whereas MP and LTH are sparse n/w methods. Which fake news detection algorithm is used in conjunction with MP and LTH? (Table 4) 6. Doc classifier exit #1 is nearly as good as exit # 2. And there's not a whole lot of difference b/w exit #1 and exit #4. Is this because document level event detection is a easy problem or something to do with the dataset? 7. Why no event level results for 90% sparsity in Table 4? Do the event level results degrade more drastically than even level as the sparsity is increased? 8. Why no results for a random sparsity pattern? That would be a good baseline for relative assessment of sparsity patterns. 9. In Tables 4,5 and 6, why is SMD 90% sparsity better than SMD 50%? 10. Looks like all sparsity patterns do almost equally well. No insight provided as to what is happening here. Is this something unique to the sparsity detection problem or is this true for GNN in general?
Section 4.3: presentation bits --> representation bits
10. Looks like all sparsity patterns do almost equally well. No insight provided as to what is happening here. Is this something unique to the sparsity detection problem or is this true for GNN in general? Section 4.3: presentation bits --> representation bits