Anomalous citations detection in academic networks
Issued Date
2024-04-01
Resource Type
ISSN
02692821
eISSN
15737462
Scopus ID
2-s2.0-85188913248
Journal Title
Artificial Intelligence Review
Volume
57
Issue
4
Rights Holder(s)
SCOPUS
Bibliographic Citation
Artificial Intelligence Review Vol.57 No.4 (2024)
Suggested Citation
Liu J., Bai X., Wang M., Tuarob S., Xia F. Anomalous citations detection in academic networks. Artificial Intelligence Review Vol.57 No.4 (2024). doi:10.1007/s10462-023-10655-5 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97882
Title
Anomalous citations detection in academic networks
Corresponding Author(s)
Other Contributor(s)
Abstract
Citation network analysis attracts increasing attention from disciplines of complex network analysis and science of science. One big challenge in this regard is that there are unreasonable citations in citation networks, i.e., cited papers are not relevant to the citing paper. Existing research on citation analysis has primarily concentrated on the contents and ignored the complex relations between academic entities. In this paper, we propose a novel research topic, that is, how to detect anomalous citations. To be specific, we first define anomalous citations and propose a unified framework, named ACTION, to detect anomalous citations in a heterogeneous academic network. ACTION is established based on non-negative matrix factorization and network representation learning, which considers not only the relevance of citation contents but also the relationships among academic entities including journals, papers, and authors. To evaluate the performance of ACTION, we construct three anomalous citation datasets. Experimental results demonstrate the effectiveness of the proposed method. Detecting anomalous citations carry profound significance for academic fairness.