Visual ranking of academic influence via paper citation. (October 2018)

• Record Type: Journal Article
• Authors: Zhou, Zhiguang; Shi, Chen; Hu, Miaoxin; Liu, Yuhua
• Is Part Of: Journal of visual languages & computing. Volume 48(2018)
• Page Start: 134
• Page End: 143
• Publication Date: 2018-10
• Subjects: Visual analysis -- Citation network -- Word2vec model -- PageRank model; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2018.08.007
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: With rapid growth of digital publishing, a great deal of document datum has been published online for a widely spread of knowledge innovations, which is an important resource for human survival and social development. However, it is a time-consuming and difficult task to conduct a high-efficiency access of valuable papers from an extremely large document database. A set of ranking techniques have been proposed to evaluate the influence of articles by counting the number and quality of citations, such as PageRank. In fact, the influence of an article does not merely depend on the account of citations, which is also highly related to the citation network. In this paper, we propose a visual analytics system for visual ranking of academic influence of articles, based on an insightful analysis of citation network. Firstly, a characterization of articles is established through word2vec model, based on an analogy between the articles in citation network and natural language processing (NPL) terms. Then, the difference between articles in the vectorized space is employed to optimize the PageRank model and achieve desired influence ranking results. A set of meaningful visual encodings are also designed to present the relationships among articles, such as the visualization of high-dimensional vectors and time-varying citation networks. At last, a visualization framework is implemented for visual ranking of academic influence of articles, with the ranking models and visual designs integrated. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in the evaluation of academic influence of articles.