  Acquiring reviewers for academic submissions is a challenging recommendation
scenario. Recent graph learning-driven models have made remarkable progress in
the field of recommendation, but their performance in the academic reviewer
recommendation task may suffer from a significant false negative issue. This
arises from the assumption that unobserved edges represent negative samples. In
fact, the mechanism of anonymous review results in inadequate exposure of
interactions between reviewers and submissions, leading to a higher number of
unobserved interactions compared to those caused by reviewers declining to
participate. Therefore, investigating how to better comprehend the negative
labeling of unobserved interactions in academic reviewer recommendations is a
significant challenge. This study aims to tackle the ambiguous nature of
unobserved interactions in academic reviewer recommendations. Specifically, we
propose an unsupervised Pseudo Neg-Label strategy to enhance graph contrastive
learning (GCL) for recommending reviewers for academic submissions, which we
call RevGNN. RevGNN utilizes a two-stage encoder structure that encodes both
scientific knowledge and behavior using Pseudo Neg-Label to approximate review
preference. Extensive experiments on three real-world datasets demonstrate that
RevGNN outperforms all baselines across four metrics. Additionally, detailed
further analyses confirm the effectiveness of each component in RevGNN.
