Computer Science ›› 2022, Vol. 49 ›› Issue (4): 88-99.doi: 10.11896/jsjkx.210300219

Special Issue: Big Data & Data Scinece

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

Survey of Visualization Methods on Academic Citation Information

ZHU Min1, LIANG Zhao-hui1,2, YAO Lin1, WANG Xiang-kun1, CAO Meng-qi1   

  1. 1 College of Computer Science, Sichuan University, Chengdu 610065, China;
    2 Wu Yuzhang Honors College, Sichuan University, Chengdu 610207, China
  • Received:2021-03-22 Revised:2021-08-06 Published:2022-04-01
  • About author:ZHU Min,born in 1971,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.Her main research interests include information visualization and visual analytics.LIANG Zhao-hui,born in 1999,is a member of China Computer Federation.His main research interests include information visualization and visual analytics.
  • Supported by:
    This work was supported by the Sichuan Science and Technology Innovation Seedling Project(2019035).

Abstract: Academic literature includes abundant citation information, which becomes the major analysis object and hot topic in both bibliometry and scientific research evaluation field.Compared with quantitative analysis methods based on mathematics and statistics, the use of visualization methods can realize the vivid presentation of citation information in time sequence and hierarchy, as well as the interactive mining of complex citation networks, which is of great significance to the reform of scientific research evaluation and the innovation of bibliometric methods.This paper introduces domestic and foreign relevant research on academic citation information analysis in recent years, summarizes the general framework of academic citation information visualization;classifies visualization methods according to two types of analysis tasks, entity evaluation and bibliometry, then elaborates the current research status, advantages and disadvantages of each type of methods, and finally points out the challenges and directions for further exploration of academic citation information visualization.

Key words: Academic citation, Bibiometry, Entity evaluation, Mapping of knowledge, Visualization method

CLC Number: 

  • TP391
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