-->PONE-D-26-21907-->-->igraph 1.0 enables fast and robust network analysis across programming languages-->-->PLOS One

Dear Dr. Zanini,

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• Kindly address the concerns raised by Reviewer#2 and Reviewer#3.

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Academic Editor

PLOS One

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Additional Editor Comments:

Thank you very much for your submission!

I request you to address the concerns of Reviewers 2 and 3 and resubmit after revising.

Thank you very much!

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Reviewers' comments:

Reviewer's Responses to Questions-->

-->Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: N/A

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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-->5. Review Comments to the Author

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Reviewer #1:  My reasons for acceptance:

1. igraph has established itself over two decades as one of the most relied-upon libraries for graph and network analysis.

2. C core for performance: the core being written in C, igraph achieves much faster performance than pure python based implementations like networkx for graph analysis and visualisation.

3. Cross-language accessibility and interoperability: igraph's support for Python, R, and Mathematica through a shared C core is a valuable design choice. There is also compatibility with networkx.

4. Open science and open source: The transparent development model reflects best practices in open science..

Reviewer #2:  Major concerns:

1. Performance comparisons are limited

The benchmarks compare igraph only against NetworkX. The introduction itself acknowledges that graph-tool is faster than NetworkX, yet graph-tool is absent from all performance comparisons. The authors should either include comparisons with graph-tool or provide a clear justification for its exclusion. Graph-tool, SNAP, and Boost Graph are discussed in the Introduction but excluded from the main performance comparisons.

2. Billion edge performance claims are incomplete

The paper demonstrates that igraph can construct graphs with billions of edges, which is impressive. However, it does not show any analytical stuffs like centrality measures, or community detection perform well at this scale. Construction and analysis are different tasks, and the latter is what most users actually need. The authors should either provide benchmarks for analytical operations at large scale or moderate their claims accordingly.

3. Benchmarking methodology needs strengthening

Each graph size was tested only twice. While the performance claims are carefully worded, more repetitions with reported variance would make the results more convincing and allow readers to assess the reliability of the reported speedups.

4. Diversity and community section lacks quantitative evidence

The manuscript discusses efforts to increase diversity and community participation, including collaboration with Women in Network Science. However, no data is provided on the impact of these initiatives. For example, has the proportion of contributions from underrepresented groups changed? Quantitative evidence would strengthen this section considerably.

5.The UMAP layout shown in Figure 2 is presented as an example of igraph's visualisation capability. However, the figure would be more informative if the UMAP layout were shown alongside other commonly used layout algorithms.

6.The interoperability example in Figure 3 demonstrates that graphs can be converted between igraph and networkx. However, for users working with large graphs, the practical feasibility of this conversion depends on its speed and memory cost. The authors should provide information on conversion speed and memory usage across a range of graph sizes, so users can make informed decisions about whether conversion is practical for their use case.

Reviewer #3:  The paper presents igraph 1.0, a general-purpose graph analysis library. igraph has been in development since the last twenty years and the current release in the paper is capable of processing graphs with billions of edges, offers interactive visualization capabilities and integrates seamlessly with notebook environments.

The paper is well-written and insightful to read. As the authors mentioned, the development of fast and consistent graph analysis libraries across programming languages is attracting increasing research attention and remains highly important particularly given the growing scale and complexity of networks enabled by modern AI and large language models (e.g., in domains such as social network analysis and bioinformatics). The framework's generic design facilitates deeper integration with machine learning ecosystems, particularly as graph neural networks have become increasingly prominent for tasks such as node classification, link prediction and other related graph-level prediction problems. In this context, igraph is a valuable component within modern graph-based analysis experiments.

The paper has the following issues (no major comments):

1. For the NetworkX comparisons, what version was tested? Have comparisons been run against NetworkX 3.0+?

2. It would be valuable for the authors to report memory usage benchmarks in addition to runtime results for billion-edge graphs.

3. The authors should also consider comparing igraph with scikit-network, which provides optimized implementations for large-scale graph mining tasks (e.g., clustering, ranking and diffusion) that are not always efficiently supported in standard graph analysis libraries.

The study represents an important contribution to advancing the development of efficient and consistent graph analysis tools across programming languages.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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igraph 1.0 enables fast and robust network analysis across programming languages

PONE-D-26-21907R1

Dear Dr. Zanini,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Amit Nanavati

Academic Editor

PLOS One

Additional Editor Comments (optional):

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