Peer code review has become a must-have in modern software development. However, many code review requests (CRRs) could be a backlog for large-scale and active projects, blocking continuous integration and continuous delivery (CI/CD). Prioritizing CRRs to make the relevant ones to be reviewed first is a critical method for addressing this issue. Early studies have shown that many factors can impact the review priority of a CRR, including its properties and relationships with others. However, the relationships, e.g., modifying the same files and sharing the same authors, are rarely considered when developing CRR prioritizers. In this paper, we propose a \underline{\textbf{G}}raph-\underline{\textbf{P}}owered \underline{\textbf{P}}rioritizer (namely \textbf{GPP}) to make full use of the properties and relationships of CRRs. GPP uses the multigraph structure to develop an initial representation of a set of CRRs and uses the graph neural network algorithm to learn the prioritization-adapted representation, and eventually, outputs an ordered list of CRRs based on it. With experimental evaluation, we define relevant CRRs in the context of CI/CD as those that are likely to achieve three objectives, i.e., being \emph{merged} while undergoing a few \emph{iterations} in a short \emph{duration}. We compare GPP against two rule-based and six learning-based prioritizers on 15 open-source software projects with more than 420K CRRs. The experimental results indicate that GPP outperforms the baselines over three basic ranking-aware evaluation metrics including NDCG (82.94%), MRR (36.52%), and MAP (63.80%); while bringing benefits in recommending best-match CRRs and balancing multiple objectives.\ \textbf{Data&materials:} \url{https://figshare.com/s/133f23da558b7b254041}