Quantifying Success in Science: An Overview

Bai, Xiaomei; Pan, Hanxiao; Hou, Jie; Guo, Teng; Lee, Ivan; Xia, Feng

doi:10.1109/access.2020.3007709

Cited by 16 publications

(8 citation statements)

References 108 publications

(123 reference statements)

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“…Four branches are separated at the end of the path, which means that these four paths have the same SPC value. Bai et al [45] build an institution citation network and a heterogeneous network composed of institutions and papers nodes. Then, a PR-based model, IPRank, was proposed to quantify the impact of institutions and papers simultaneously.…”

Section: Resultsmentioning

confidence: 99%

Knowledge diffusion trajectories of PageRank: A main path analysis

Yan

2023

Journal of Information Science

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In the era of the Internet and big data, the PageRank (PR) algorithm is a constantly evolving research field. However, there is no systematic research to explore the overall development trend of the PR domain. This article evaluates 1446 articles related to the PR algorithm and provides a thorough understanding of the PR field through the main path analysis (MPA). Through two basic main paths, a number of papers that play a leading role have been identified, which outline the backbone of the PR domain. Based on the analysis of multiple main paths, four main subareas have been investigated. There are accelerating the computation of PR, comprehensive applications of PR, researches on academic impact assessment and age preference in network evolution. Finally, this article discusses the research findings and the future directions of the PR field. It is the first attempt to identify the development trend of the PR domain through MPA, thus providing an insight into the knowledge evolution of the PR field over the past two decades.

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Section: Resultsmentioning

confidence: 99%

Knowledge diffusion trajectories of PageRank: A main path analysis

Yan

2023

Journal of Information Science

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“…2 In this paper, we regard the abovementioned citations as anomalous citations. Anomalous citations are academic misconducts that are currently highly concerned by the academic community (Franck 1999;Bai et al 2016;Chorus and Waltman 2016;Mimouni et al 2016;Bai et al 2017Bai et al , 2020Liu et al 2022). It is essential to detect anomalous citations because they will bring a lot of negative effects.…”

Section: Introductionmentioning

confidence: 99%

Anomalous citations detection in academic networks

Liu,

Bai,

Wang

et al. 2024

Artif Intell Rev

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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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“…Traditionally, the quantity of citations is one measure of the quality of a paper, or the impact of academic entities [4]. For example, existing bibliometric indicators such as h-index, gindex, and Journal Impact Factor (JIF) all focus on quantitative evaluation aspect while ignoring qualitative aspects.…”

Section: Introductionmentioning

confidence: 99%

Deep Graph Learning for Anomalous Citation Detection

Liu

Xia

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

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Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, i.e., anomaly detection in a citation network. Citation is considered as one of the most crucial metrics to evaluate the impact of scientific research, which may be gamed in multiple ways. Therefore, anomaly detection in citation networks is of significant importance to identify manipulation and inflation of citations. To address this open issue, we propose a novel deep graph learning model, namely GLAD (Graph Learning for Anomaly Detection), to identify anomalies in citation networks. GLAD incorporates text semantic mining to network representation learning by adding both node attributes and link attributes via graph neural networks. It exploits not only the relevance of citation contents but also hidden relationships between papers. Within the GLAD framework, we propose an algorithm called CPU (Citation PUrpose) to discover the purpose of citation based on citation texts. The performance of GLAD is validated through a simulated anomalous citation dataset. Experimental results demonstrate the effectiveness of GLAD on the anomalous citation detection task.

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Quantifying Success in Science: An Overview

Cited by 16 publications

References 108 publications

Knowledge diffusion trajectories of PageRank: A main path analysis

Knowledge diffusion trajectories of PageRank: A main path analysis

Anomalous citations detection in academic networks

Deep Graph Learning for Anomalous Citation Detection

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