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A `peer-review system' in the context of judging research contributions, isone of the prime steps undertaken to ensure the quality of the submissionsreceived, a significant portion of the publishing budget is spent towardssuccessful completion of the peer-review by the publication houses.Nevertheless, the scientific community is largely reaching a consensus thatpeer-review system, although indispensable, is nonetheless flawed. A verypertinent question therefore is "could this system be improved?". In thispaper, we attempt to present an answer to this question by considering amassive dataset of around $29k$ papers with roughly $70k$ distinct reviewreports together consisting of $12m$ lines of review text from the Journal ofHigh Energy Physics (JHEP) between 1997 and 2015. In specific, we introduce anovel \textit{reviewer-reviewer interaction network} (an edge exists betweentwo reviewers if they were assigned by the same editor) and show thatsurprisingly the simple structural properties of this network such as degree,clustering coefficient, centrality (closeness, betweenness etc.) serve asstrong predictors of the long-term citations (i.e., the overall scientificimpact) of a submitted paper. These features, when plugged in a regressionmodel, alone achieves a high $R^2$ of \0.79 and a low $RMSE$ of 0.496 inpredicting the long-term citations. In addition, we also design a set ofsupporting features built from the basic characteristics of the submittedpapers, the authors and the referees (e.g., the popularity of the submittingauthor, the acceptance rate history of a referee, the linguistic propertiesladen in the text of the review reports etc.), which further results in overallimprovement with $R^2$ of 0.81 and $RMSE$ of 0.46.

Influence of Reviewer Interaction Network on Long-term Citations: A Case  Study of the Scientific Peer-Review System of the Journal of High Energy  Physics