All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
Thank you! I believe you addressed all comments from the AE and the reviewers adequately. The manuscript is ready for publication.
[# PeerJ Staff Note - this decision was reviewed and approved by Daniel Katz, a 'PeerJ Computer Science' Section Editor covering this Section #]
The reviewer sees a major improvement in the manuscript. Yet, there are still several shortcomings that led me to a major revision. In particular, please address:
- Introduction of important AI techniques used
- Include a discussion of more recent literature related to your topic.
The proposed scheme is presented in great detail, but the language needs substantial improvement.
The experimental design is logical and interesting.
The findings are neutral, and if possible, more state-of- the-art models should be introduced for comparison.
1). The format of the manuscript needs to be further modified, and there are still many issues, such as: the first sentence of each paragraph needs to be indent, the line spacing of the whole paper is not uniform, such as “lines 59-65,375-378”, and the format of Algorithm1 is not aligned, such as lines 9-10.
2) The organizational structure of this paper is somewhat chaotic, and the authors need to reconsider. For example, the contents of "1. introduction" and "1.1 Relate Work" are usually placed in two different sections.
3). The cited references are not advanced enough, especially the number of articles cited in recent years is seriously insufficient, which cannot fully support the innovation of the method proposed in this study.
4) The "Figure 1" suggestion can be changed to a pictograph, which will be more intuitive; "Figure 2" and "Figure 3" need to improve the graphics quality.
Both reviewers raised critical comments. As a result, the current version cannot be accepted.
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
In this paper, the authors propose the NuQClqF1 and NuQClqR1 algorithms, and applies them to guide γ- quasi - clique search algorithm. Compared with the existing NuQClq algorithm, the proposed algorithm adds a simple fitness weighting method to update individuals in the current iteration. Experimental studies have already demonstrated the superiority of the proposed strategy and algorithm. Although the proposed NuQClqF1 andNuQclqR1algorithm seems meaningful, it still lacks sufficient theoretical and experimental support. Furthermore, the proposed model is only the simple combination of existing model. In addition, the expression of this manuscript is fair and wordy. The language should be greatly improved.
More comparative experiments with benchmarks need to be conducted to further illustrate the advantages of this method.
This study only conducted a series of tests on γ-quasi-clique with strong constraints, and how to prove the effectiveness of the proposed method needs further clarification.
Major Points
(1) According to the experimental data in Table 1, the two methods proposed in this paper are not significantly superior to the NuQClq algorithm, and it can be seen from Table 2 that the proposed methods are more time-consuming.
(2) This study only conducted a series of tests on γ-quasi-clique with strong constraints, and how to prove the effectiveness of the proposed method needs further clarification.
(3) In the experimental part, more comparative experiments with benchmarks need to be conducted to further illustrate the advantages of this method.
Minor Points
(1) The expression of Abstract is fair and wordy. The language should be greatly improved.
(2) It is recommended to use serial number to mark the organizational structure of the article clearly.
(3) Some formats need to be standardized. Such as in Line 65 "(G[S]) ≥γ"; In Line 268 "NP"; And in Line 271 "in the (t_2), (t_1), t and(t+1) iterations". Double-check the whole manuscript.
(4) In Table 1, it is recommended to use the standard three-wire Table.
68-> Related instead of Relate
325 -> Information instead of Imformation
326 -> Same as above
The benchmarks are incorrectly run. The average time is of less interest than the average time over many evaluations. Perhaps ASV (airspeed velocity) could be used. At the very least a graph of the time taken per problem per algorithm with agglomeration is required.
In some sense, new information of course is always useful to optimization algorithms. However, the costs and tradeoffs need to better explained. There is only a quick mention in 385 about the search time. Please quantify this cost more concretely, how much history is required? What is the complexity (time / memory)? Also, for a max iteration based approach, I would request that you consider the case of low and high max iterations.
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.