Peer Review History
Original SubmissionAugust 16, 2019 |
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PONE-D-19-23200 Multidimensional diffusion processes in dynamic online networks PLOS ONE Dear Professor Easley, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. A number of issues have been raised by the two referees which need to be carefully addressed. In particular one of the referee ask to have access to the replication code. Please allow the referee to double check it. We would appreciate receiving your revised manuscript by Nov 23 2019 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols Please include the following items when submitting your revised manuscript:
Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. We look forward to receiving your revised manuscript. Kind regards, Massimo Riccaboni Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 1. We note that you have indicated that data from this study are available upon request. 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Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. We will update your Data Availability statement on your behalf to reflect the information you provide. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ********** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ********** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ********** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ********** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Thank you for giving me the opportunity to read this interesting paper. ## What I really liked I really like the matching procedure and the robustness checks, this must have been an enormous amount of work. I especially liked the usage of collaborative filtering in conjunction with a regime of robustness checks, well done. Given the complexity of the analysis and the numerous steps required to obtain the results, I would like to see the source code. Moreover I would strongly suggest to make this public after publication (e.g., using GH). ## Major items The RQ is: To what extend does peer-influence affect item adoption on GH. My first comments relates to the usage of "item adoption". Being nearly a decade old user of GH myself I have seen people use the "staring" system in a number of ways. Moreover, the platform mechanisms, by which repos are suggested or highlighted to the user, has changed over time. Regarding the star system: - Some users on GH, use stars to *bookmark* interesting projects, and not to signal that they are or will use it - Not all users who end up using the software in a repo star it. - Not all repos are something to be used/adopted, but read (e.g., https://github.com/sindresorhus/awesome (116k stars at the time of writing)) - Some users, confuse the purpose of forks and stars and end up forking, when in reality they wanted to signal support. What I want to say with this, is that equating "staring a repo" and "item usage" is not correct. The work is still very valuable, however I would not use "item adoption" as the motivation, since the analogy to products is strenuous and as argued above "starring" is not the same as "usage". The matching procedure is quite complex, and I had to reread it several times, and am still not sure I got all the steps, hence I would like to see the replication code. An illustration (i.e., cartoon) of the steps would go a long way to clarify the procedure. Why do you choose this particular period (February, 2011 to October, 2013) for the analysis? A sentence justifying this choice is necessary. ## Minor items Regarding the role of "leaders": Follower relations, like on twitter can be artificial. On GH, there are people with thousands of followers who follow just as many, therefore these follower relationships likely do not represent meaningful relations. You use the term "preference types", without clear definition. In line 48 you say that MS is interested in using the platform. You should note in the text that MS is the owner of GH as of 2019, or avoid the mentioning of MS. - Fig.1(b) Not clear how to read this plot. It is a CCDF of something, not clear why CCDF and not PDF (histogram) - Fig.1(d) something is missing: Where can I see the control group? There is only one curve in Figure~1~(d). ## Suggestion for future work The following is just a suggestion, and in no way a critique of this work. If the matching is done sufficiently in advance, the estimation could be turned into a Diff-in-Diff specification. Here you would match treated and controls in a "recruitment phase" several months before the event, then look at "adoption" in the pre-treatment (after recruitment, before event) phase and post treatment phase. Then you can compare pre-treatment and post-treatment "adoption" across treated and controls. This is just a suggestion, and in no way a critique of this work. Reviewer #2: The paper describes an interesting application of machine learning to causal inference for dynamic online networks, where numerous items diffuse simultaneously. Here, machine learning is used to infer agents' preferences (based on previously adopted decisions), which are subsequently used to match "treated" and "untreated" agents, and estimate causal effects. Here, "treated" means basically that one or more of the agents followed by the agent has recently adopted a specific item. My major commet is that, in my opinion, the paper would improve a lot its readability (at least for a reader interested in technical details) by including in the main text some formulas (at least the most important ones): in this way, it would be easier for the reader to identify more quickly which are the variables of most interest in the analysis. If including other formulas were not possible (e.g., to make the paper more readable also to readers less interested in technical details), it would be nice at least to include a figure/diagram describing the basic steps of the proposed method, containing links to the parts of the paper where such steps are detailed. The following are some other minor issues that the authors could consider to further improve the paper: - sometimes, an empty space appears before a numbered reference (e.g., ( [2])). It would be better to remove it; - by reading Section 1, one can conclude that "homophily" and "peer influence" are among the most important variables in the model. A link to their precise definitions (e.g., to the sections of the text/appendices where they are defined) would be very useful to correctly interpret the proposed model; - the authors may wish to include some recent references about machine learning applied to causal inference, especially in the presence of heterogeneous causal effects; - line 122: the term "relative peer influence" appears here for the first time: before, it was called "peer influence" (without "relative"); - line 14: "We will then include": i.e., in a second analysis? - line 143: "Note that, unlike [3]": why "unlike" [3]? Here the authors should describe more clearly the difference between their model and the one contained in [3]; - line 161: "one possible approach would be to utilize the programming languages of past adoptions" -> this sentence could be improved by specifying that this is the case, e.g., of the GitHub application described later in the text; - paragraph starting on line 201: in machine-learning terms, the "test set" mentioned here could be more properly called "validation set" (although some references in statistics still reserve the name "test set" to the case considered here). It is not clear, however, if "cross" validation os simply the hold out method has been performed; - what is the interpretation of the off-diagonal elements of the matrix \\Sigma on line 213? Or has \\Sigma to be diagonal? - in the paragraph starting on line 301, some more details about the shuffle test could be added; - in the current version of the work, all the figures have been reported at the end of it, making it quite difficult to follow the part of the text where those figures are commented. ********** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step. |
Revision 1 |
Multidimensional diffusion processes in dynamic online networks PONE-D-19-23200R1 Dear Dr. Easley, We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements. Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication. Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. With kind regards, Massimo Riccaboni Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: |
Formally Accepted |
PONE-D-19-23200R1 Multidimensional diffusion processes in dynamic online networks Dear Dr. Easley: I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. For any other questions or concerns, please email plosone@plos.org. Thank you for submitting your work to PLOS ONE. With kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Massimo Riccaboni Academic Editor PLOS ONE |
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