Response to Meta-Reviewer

General Comments. I wish to inform you that the acceptance or rejection of your manuscript is still UNDECIDED (we don’t use "major revisions" or "minor
revisions").

Response: We appreciate your handling of the review process.

Comment 1
The contribution relative to the previously published dataset INDoRI should be clarified, along with claim that the datasets have a graph based representation
(See Reviewer 1 comments)

Response:
We appreciate the meta-reviewer’s comments. In the revised manuscript, we modified the abstract as well as changed the narrative structure of the introduction to clarify our contribution related to the INDoRI dataset. We have also expanded the discussion on data curation and quality control to ensure transparency.
Additionally, we have clearly established the graph-based representation of the dataset, explaining how ingredient networks were constructed and analyzed. Furthermore, we have addressed all the concerns raised by Reviewer 1, specifically focusing on the dataset structure, methodology, and its positioning within the existing literature.

Comment 2
The Lack of Interpretation and Justification should be addressed - in particular with respect to Social Behaviour and Community Structure.

Response:
In the revised manuscript, we have significantly enhanced the interpretation and justification of our findings, particularly in relation to social behavior and community structure. We have expanded the discussion on how ingredient networks exhibit characteristics similar to social networks, explaining the implications of clustering, connectivity, and modularity in the context of culinary traditions. Additionally, we have provided a more detailed analysis of the community structure,
drawing connections between ingredient groupings and cultural, regional, and functional aspects of cooking. [The same is reflected in the Results & Analysis and Discussion Section on page 8 and 15 of the revised manuscript]

Comment 3
The link to ‘social’ networks should be underpined by additional theoretical grounding or the claim should be relaxed.

Response:
We appreciate the meta-reviewer’s comment regarding the theoretical grounding of the link to social networks. In the revised manuscript, we have strengthened our argument by providing additional statistical validation of the scale-free nature of ingredient networks. From the linear regression analysis performed on the log-transformed data across 10 global cuisine ingredient networks, we observed a consistent range of values for the slope (-2.45 to -2.68), intercept (0.18 to 0.22), and high R-squared values (0.9965 to 0.9991), indicating an excellent fit between the log-transformed degree sequence and degree distribution. Furthermore, the extremely low p-values (10−25 to 10−30) provide strong statistical evidence supporting the robustness of our findings. These results reinforce the argument that ingredient networks exhibit power-law behavior, a fundamental property observed in many real-world social networks. By integrating these statistical validations, we have strengthened the theoretical grounding of our claims while ensuring that our conclusions remain well-supported and justified. [The same is reflected in the Degree Distribution of InN subsection of Result & Discussion Section on page 8 of the revised manuscript]

Comment 4
There a number of gaps in the related work and the contribution of the paper needs updated accordingly

Response:
In the revised manuscript, we have incorporated additional literature, including the references suggested by the reviewers, to better contextualize our study within the
existing body of research. This collection includes key works on ingredient networks, food pairing principles, and computational gastronomy, which help bridge the gap between previous studies and our contributions. By integrating these references, we have strengthened the positioning of our work, clearly outlining how our study extends beyond existing research. This ensures that our contributions are well-differentiated and highlights the novelty of our findings in the domain of ingredient network analysis. [The same is reflected in the Related Work Section on page 4 of the revised manuscript]

Comment 5
The overall technical rigour of the manuscript needs significant improvement and comparisons across cuisines types should be more systematic and consistent

Response:
In the previous version of the manuscript, we have analyzed 10 global cuisines. In the revised version, we have enhanced the systematic comparison of ingredient networks across different cuisine types by ensuring consistency in the evaluation metrics and analysis framework. We have refined the result and discussion sections, providing clearer justifications for our analytical choices and ensuring uniformity in metric reporting (e.g., degree distribution, clustering coefficient, eigen centrality, and community structure). Additionally, we have strengthened the statistical validation of our findings, reinforcing the robustness of our cross-cuisine comparisons. These improvements collectively enhance the rigor and clarity of our study. [The same is reflected in the Results & Analysis and Discussion Section on page 8 and 15 of the revised manuscript]

Note:
The individual reviewer comments with our responses, along with the revised manuscript, are included in the attached ZIP file.