Revealing the character of journals in higher-order citation networks

Xiang Li (), Chengli Zhao, Zhaolong Hu, Caixia Yu and Xiaojun Duan ()
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Xiang Li: National University of Defense Technology
Chengli Zhao: National University of Defense Technology
Zhaolong Hu: Zhejiang Normal University
Caixia Yu: Zhejiang Normal University
Xiaojun Duan: National University of Defense Technology

Scientometrics, 2022, vol. 127, issue 11, No 14, 6315-6338

Abstract: Abstract Revealing the character of journals based on citation data remains an interesting issue nowadays. It aims to establish a reasonable journal evaluation system and provides suitable journals for scholars to submit to. As for traditional methods, based on first-order citation networks, they are poor at describing the multivariate sequential interactions among journals and at revealing their character. In this article, an efficient approach, namely, the recombination higher-order network algorithm, is proposed to well reveal the importance and complexity of journals in citation networks. Through the recombination of citation flow, the multivariate sequential data will be collected, which is a key step to structure a higher-order citation network. Combining with network topology features, the importance evaluation metrics are proposed from local and global perspectives respectively. The experiments in the empirical network demonstrate that compared with traditional methods, our method works better in identifying important journals. Besides, the higher-order complexity metric and the higher-order simplicity metric are designated as the complexity or simplicity evaluation metric in higher-order networks respectively, which better identify journal categories.

Keywords: Higher-order network; Citation network; Multivariate sequential interaction; Vital nodes identification; Second-order self-citation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11192-022-04518-z

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