In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniq...In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.展开更多
Purpose:The goal of this study is a comparative analysis of the relation between funding(a main driver for scientific research)and citations in papers of Nobel Laureates in physics,chemistry and medicine over 2019-202...Purpose:The goal of this study is a comparative analysis of the relation between funding(a main driver for scientific research)and citations in papers of Nobel Laureates in physics,chemistry and medicine over 2019-2020 and the same relation in these research fields as a whole.Design/methodology/approach:This study utilizes a power law model to explore the relationship between research funding and citations of related papers.The study here analyzes 3,539 recorded documents by Nobel Laureates in physics,chemistry and medicine and a broader dataset of 183,016 documents related to the fields of physics,medicine,and chemistry recorded in the Web of Science database.Findings:Results reveal that in chemistry and medicine,funded researches published in papers of Nobel Laureates have higher citations than unfunded studies published in articles;vice versa high citations of Nobel Laureates in physics are for unfunded studies published in papers.Instead,when overall data of publications and citations in physics,chemistry and medicine are analyzed,all papers based on funded researches show higher citations than unfunded ones.Originality/value:Results clarify the driving role of research funding for science diffusion that are systematized in general properties:a)articles concerning funded researches receive more citations than(un)funded studies published in papers of physics,chemistry and medicine sciences,generating a high Matthew effect(a higher growth of citations with the increase in the number of papers);b)research funding increases the citations of articles in fields oriented to applied research(e.g.,chemistry and medicine)more than fields oriented towards basic research(e.g.,physics).Practical implications:The results here explain some characteristics of scientific development and diffusion,highlighting the critical role of research funding in fostering citations and the expansion of scientific knowledge.This finding can support decision-making of policymakers and R&D managers to improve the effectiveness in allocating financial resources in science policies to generate a higher positive scientific and societal impact.展开更多
A novel indicator called price-citation was proposed.Based on the company integrated patent database of China listed companies of common stocks(A-shares)with the stock price and the stock return rate data,more than tw...A novel indicator called price-citation was proposed.Based on the company integrated patent database of China listed companies of common stocks(A-shares)with the stock price and the stock return rate data,more than two thousand of A-shares from 2017 to 2020 were selected.The effect of the traditional patent forward citation and the price-citation for discriminating the stock return rate was thoroughly analyzed via ANOVA.The A-shares of forward citation counts above the average showed higher stock return rate means than the A-shares having patents but receiving no forward citations.The price-citation,combining both the financial and patent attributes,defined as the multiplication of the current stock price and the currently receiving forward citation count,showed its excellence in discriminating the stock return rate.The A-shares of higher price-citation showed significantly higher stock return rate means while the A-shares of lower price-citation showed significantly lowest stock return rate means.The price-citation effect had not been changed by COVID-19 though COVID-19 affected the social and economic environment to a considerable extent in 2020.展开更多
基金supported by the National Natural Science Foundation of China(No.62271274).
文摘In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.
文摘Purpose:The goal of this study is a comparative analysis of the relation between funding(a main driver for scientific research)and citations in papers of Nobel Laureates in physics,chemistry and medicine over 2019-2020 and the same relation in these research fields as a whole.Design/methodology/approach:This study utilizes a power law model to explore the relationship between research funding and citations of related papers.The study here analyzes 3,539 recorded documents by Nobel Laureates in physics,chemistry and medicine and a broader dataset of 183,016 documents related to the fields of physics,medicine,and chemistry recorded in the Web of Science database.Findings:Results reveal that in chemistry and medicine,funded researches published in papers of Nobel Laureates have higher citations than unfunded studies published in articles;vice versa high citations of Nobel Laureates in physics are for unfunded studies published in papers.Instead,when overall data of publications and citations in physics,chemistry and medicine are analyzed,all papers based on funded researches show higher citations than unfunded ones.Originality/value:Results clarify the driving role of research funding for science diffusion that are systematized in general properties:a)articles concerning funded researches receive more citations than(un)funded studies published in papers of physics,chemistry and medicine sciences,generating a high Matthew effect(a higher growth of citations with the increase in the number of papers);b)research funding increases the citations of articles in fields oriented to applied research(e.g.,chemistry and medicine)more than fields oriented towards basic research(e.g.,physics).Practical implications:The results here explain some characteristics of scientific development and diffusion,highlighting the critical role of research funding in fostering citations and the expansion of scientific knowledge.This finding can support decision-making of policymakers and R&D managers to improve the effectiveness in allocating financial resources in science policies to generate a higher positive scientific and societal impact.
基金support from Ministry of Science and Technology,Taiwan,R.O.C.under Grant No.MOST 109-2410-H-011-021-MY3.
文摘A novel indicator called price-citation was proposed.Based on the company integrated patent database of China listed companies of common stocks(A-shares)with the stock price and the stock return rate data,more than two thousand of A-shares from 2017 to 2020 were selected.The effect of the traditional patent forward citation and the price-citation for discriminating the stock return rate was thoroughly analyzed via ANOVA.The A-shares of forward citation counts above the average showed higher stock return rate means than the A-shares having patents but receiving no forward citations.The price-citation,combining both the financial and patent attributes,defined as the multiplication of the current stock price and the currently receiving forward citation count,showed its excellence in discriminating the stock return rate.The A-shares of higher price-citation showed significantly higher stock return rate means while the A-shares of lower price-citation showed significantly lowest stock return rate means.The price-citation effect had not been changed by COVID-19 though COVID-19 affected the social and economic environment to a considerable extent in 2020.