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Influential scientific papers tend to be primarily based on combinations of prior works. However, assessing the potential impact of a new scientific paper remains a challenging task. In this article, we introduce an innovative framework to investigate the relationship between the embedding of citation networks and a paper’s future citation counts, based on the graph representation learning approach. First, we employ three Nobel Prize-winning topic papers from the Web of Science as our data source. Through data preprocessing and direct citation network modelling, we train the struc2vec model to obtain embeddings of papers’ citation network structure. Then, we perform visualisation and analysis on two types of networks. One is the direct-citation network, in which we identify four patterns of linkage between newly published papers and existing knowledge, and the other is the co-citation network, where we measure three structural variation indicators of new papers based on existing research findings. Finally, a statistical test is used to examine the predictive potentials of network embeddings. The results demonstrate that the structural features captured by the graph representation learning model can be used to predict a paper’s citation counts and impact. This article innovatively combines cluster analysis, visual analysis and statistical analysis to gain insights into the relationship between the hard-to-explain structural embeddings of newly published papers in a citation network and their future citations.
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