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However, existing studies merely work under a single view, which neglects the rich information hidden in other aspects of the graph. Hence, in this work, we propose a <jats:bold>M<\/jats:bold>ultiview <jats:bold>C<\/jats:bold>ontrastive <jats:bold>R<\/jats:bold>epresentation <jats:bold>L<\/jats:bold>earning (MCRL) model to obtain representations of nodes from multiple perspectives and then infer the node importance. Specifically, we are the first to apply the contrastive learning technique to the node importance analysis task, which enhances the expressiveness of graph representations and lays the foundation for importance estimation. Moreover, based on the improved representations, we generate the entity importance score by attentively aggregating the scores from two different views, i.e., node view and node\u2010edge interaction view. 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