Anomalous citations detection in academic networks

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2024
Autor:innen
Liu, Jiaying
Bai, Xiaomei
Tuarob, Suppawong
Xia, Feng
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National Natural Science Foundation of China: No. 72204037
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Artificial Intelligence Review. Springer. 2024, 57(4), 103. eISSN 1573-7462. Available under: doi: 10.1007/s10462-023-10655-5
Zusammenfassung

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.

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004 Informatik
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Anomalous citation, Scholarly big data, Network representation, Nonnegative matrix factorization
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ISO 690LIU, Jiaying, Xiaomei BAI, Mengying WANG, Suppawong TUAROB, Feng XIA, 2024. Anomalous citations detection in academic networks. In: Artificial Intelligence Review. Springer. 2024, 57(4), 103. eISSN 1573-7462. Available under: doi: 10.1007/s10462-023-10655-5
BibTex
@article{Liu2024-03-29Anoma-69874,
  year={2024},
  doi={10.1007/s10462-023-10655-5},
  title={Anomalous citations detection in academic networks},
  number={4},
  volume={57},
  journal={Artificial Intelligence Review},
  author={Liu, Jiaying and Bai, Xiaomei and Wang, Mengying and Tuarob, Suppawong and Xia, Feng},
  note={Article Number: 103}
}
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