Measuring the scientific impact of academic papers based on weighted heterogeneous scholarly networkOA

Purpose:Ranking scientific papers is an important issue for all disciplines.Although this research problem has been intensively studied,the best ranking algorithm is still to be discovered.Some existing studies have proposed evaluation algorithms based on heterogeneous scholarly networks to measure the influence of papers.However,these algorithms have not fully considered the weighted characteristics of different scholarly networks,which can lead to biased ranking results.Evaluation algorithms based on weighted heterogeneous scholarly networks need to be further developed to address this limitation.Design/methodology/approach:We propose a weighted heterogeneous network-based ranking algorithm to evaluate the impact of scientific papers,considering the mutual reinforcement relationship among the influences of different academic entities.The weighted heterogeneous scholarly network we constructed considers factors such as the similarity between papers,the number of authors in the papers,and the number of papers published by the authors,which can effectively reflect the relationships among papers,authors,and journals.Findings:We applied the proposed algorithm to the American Physical Society(APS)dataset and verified the effectiveness of this method.The empirical results indicate that,compared with other mutual enhancement algorithms,our proposed algorithm can better identify recognized high-impact papers and perform well in evaluating both author and journal impacts.Research limitations:The proposed algorithm has only been verified in the domain of physics,and further validation of the algorithm’s effectiveness is needed in other disciplines.Practical implications:This research can deepen our understanding of impact evaluation and help propose better evaluation methods.The proposed algorithm can be applied to identify important papers in the field of physics and recommend them to relevant scientists.Originality/value:This study considers the weighted characteristics of different academic networks more comprehensively and,on this basis,proposes a paper impact evaluation algorithm based on weighted heterogeneous networks by leveraging the mutual reinforcement relationship of the influence of different academic entities.