Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (3): 81-89.doi: 10.11896/jsjkx.230100037

• Database & Big Data & Data Science • Previous Articles     Next Articles

Academic Influence Ranking Algorithm Based on Topic Reputation and Dynamic HeterogeneousNetwork

CHEN Pan1, CHEN Hongmei2,3,4,5, LUO Chuan6   

  1. 1 Tangshan Research Institute,Southwest Jiaotong University,Tangshan,Hebei 063000,China
    2 School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China
    3 Engineering Research Center of Sustainable Urban Intelligent Transportation,Ministry of Education,Chengdu 611756,China
    4 National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Southwest Jiaotong University,Chengdu 611756,China
    5 Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratoryof Sichuan Province,Southwest Jiaotong University,Chengdu 611756,China
    6 College of Computer Science,Sichuan University,Chengdu 610065,China
  • Received:2023-01-09 Revised:2023-05-25 Online:2024-03-15 Published:2024-03-13
  • About author:CHEN Pan,born in 1997,postgraduate.His main research interests include data mining and academic big data.CHEN Hongmei,born in 1971,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.19214M).Her main research interests include intelligent information processing,pattern recognition,etc.
  • Supported by:
    National Natural Science Foundation of China(61976182,62076171),Natural Science Foundation of Sichuan Province(2022NSFSC0898) and Sichuan Science and Technology Achievements Transfer and Transformation Demonstration Project(2022ZHCG0005).

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Abstract: 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 consideration of text contents or the dynamic changes of academic network structure.To solve this problem,this paper proposes an algorithm 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 algorithm 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 graded 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 influence of papers.

Key words: Heterogeneous network, Academic influence, Academic big data, Thematic prestige, Thesis ranking

CLC Number: 

  • TP391
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