Academic Influence Ranking Algorithm Based on Topic Reputation and Dynamic Heterogeneous Network
Effectively mining academic big data and analyzing academic influence of papers are benefical for researchers to obtain important information.The dynamic changes of text content and academic network structure have an important impact on the ranking results of academic impact.However,the existing ranking algorithms of academic influence of papers either lack consider-ation of text contents or the dynamic changes of academic network structure.To solve this problem,this paper proposes an algo-rithm for ranking academic influence,which is called TND-Rank,based on topic reputation and dynamic heterogeneous network.In TND-Rank,the impact of the topic on the paper at a certain time is measured and embedded to the paper influence ranking al-gorithm that takes into account the time factor.The dynamic ranking related to the academic impact of a paper is calculated by comprehensively considering the influence of various factors,i.e,the level of topic prestige,journal,author,and time etc.In the experiments,the AMiner data set published between 1936 and 2014 with complete information are analyzed,and compared with four related algorithms in recent years.Spearman correlation coefficient,normalized discounted cumulative gain(NDCG)and gra-ded average precision(GAP)are adopted to evaluate performance of the algorithm.Experimental results verify the feasibility and effectiveness of the proposed algorithm TND-Rank,which can effectively synthesize various information to rank the academic in-fluence of papers.
Heterogeneous networkAcademic influenceAcademic big dataThematic prestigeThesis ranking
万方数据
维普
