  A `peer-review system' in the context of judging research contributions, is
one of the prime steps undertaken to ensure the quality of the submissions
received, a significant portion of the publishing budget is spent towards
successful completion of the peer-review by the publication houses.
Nevertheless, the scientific community is largely reaching a consensus that
peer-review system, although indispensable, is nonetheless flawed. A very
pertinent question therefore is "could this system be improved?". In this
paper, we attempt to present an answer to this question by considering a
massive dataset of around $29k$ papers with roughly $70k$ distinct review
reports together consisting of $12m$ lines of review text from the Journal of
High Energy Physics (JHEP) between 1997 and 2015. In specific, we introduce a
novel \textit{reviewer-reviewer interaction network} (an edge exists between
two reviewers if they were assigned by the same editor) and show that
surprisingly the simple structural properties of this network such as degree,
clustering coefficient, centrality (closeness, betweenness etc.) serve as
strong predictors of the long-term citations (i.e., the overall scientific
impact) of a submitted paper. These features, when plugged in a regression
model, alone achieves a high $R^2$ of \0.79 and a low $RMSE$ of 0.496 in
predicting the long-term citations. In addition, we also design a set of
supporting features built from the basic characteristics of the submitted
papers, the authors and the referees (e.g., the popularity of the submitting
author, the acceptance rate history of a referee, the linguistic properties
laden in the text of the review reports etc.), which further results in overall
improvement with $R^2$ of 0.81 and $RMSE$ of 0.46.
