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  • Huang, Yong  (7)
  • Lu, Wei  (7)
  • General works  (7)
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  • General works  (7)
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  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2018
    In:  Scientometrics Vol. 117, No. 3 ( 2018-12), p. 2177-2193
    In: Scientometrics, Springer Science and Business Media LLC, Vol. 117, No. 3 ( 2018-12), p. 2177-2193
    Type of Medium: Online Resource
    ISSN: 0138-9130 , 1588-2861
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 2018679-4
    detail.hit.zdb_id: 435652-4
    SSG: 11
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2018
    In:  Scientometrics Vol. 115, No. 1 ( 2018-4), p. 463-486
    In: Scientometrics, Springer Science and Business Media LLC, Vol. 115, No. 1 ( 2018-4), p. 463-486
    Type of Medium: Online Resource
    ISSN: 0138-9130 , 1588-2861
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 2018679-4
    detail.hit.zdb_id: 435652-4
    SSG: 11
    Location Call Number Limitation Availability
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  • 3
    Online Resource
    Online Resource
    SAGE Publications ; 2020
    In:  Journal of Information Science Vol. 46, No. 6 ( 2020-12), p. 837-848
    In: Journal of Information Science, SAGE Publications, Vol. 46, No. 6 ( 2020-12), p. 837-848
    Abstract: Traditionally, publication citation networks are regarded as acyclic, that is, no loops in the network as an earlier published article cannot cite a later published article. However, due to the accessibility of pre-print versions of articles, there might be some loops in a publication citation network. This article presents a descriptive statistic on loops in publication citation networks of computer science and physics by employing a network-based indicator, namely, strongly connected component (SCC). By employing computer science and physics disciplines publications from the Web of Science database as examples, this article examines the count of loops, how the count changes over time and how the count relates to the published year difference between publications within the loop in the citation network. Some common structural patterns are also extracted and analysed; we observe that the two disciplines share the most frequent patterns though there exist some minor differences. Moreover, we find that self-citations in terms of authors, authors’ institutions and journals contribute to the formation of loops in publication citation networks.
    Type of Medium: Online Resource
    ISSN: 0165-5515 , 1741-6485
    RVK:
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
    detail.hit.zdb_id: 439125-1
    detail.hit.zdb_id: 2025062-9
    SSG: 24,1
    Location Call Number Limitation Availability
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  • 4
    Online Resource
    Online Resource
    SAGE Publications ; 2021
    In:  Journal of Information Science Vol. 47, No. 5 ( 2021-10), p. 615-626
    In: Journal of Information Science, SAGE Publications, Vol. 47, No. 5 ( 2021-10), p. 615-626
    Abstract: This article defines and explores the direct citations between citing publications (DCCPs) of a publication. We construct an ego-centred citation network for each paper that contains all of its citing papers and itself, as well as the citation relationships among them. By utilising a large-scale scholarly dataset from the computer science field in the Microsoft Academic Graph (MAG-CS) dataset, we find that DCCPs exist universally in medium and highly cited papers. For those papers that have DCCPs, DCCPs do occur frequently; highly cited papers tend to contain more DCCPs than others. Meanwhile, the number of DCCPs of papers published in different years does not vary dramatically. This paper also discusses the relationship between DCCPs and some indirect citation relationships (e.g. co-citation and bibliographic coupling).
    Type of Medium: Online Resource
    ISSN: 0165-5515 , 1741-6485
    RVK:
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 439125-1
    detail.hit.zdb_id: 2025062-9
    SSG: 24,1
    Location Call Number Limitation Availability
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  • 5
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2020
    In:  Scientometrics Vol. 124, No. 3 ( 2020-09), p. 1923-1943
    In: Scientometrics, Springer Science and Business Media LLC, Vol. 124, No. 3 ( 2020-09), p. 1923-1943
    Type of Medium: Online Resource
    ISSN: 0138-9130 , 1588-2861
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2018679-4
    detail.hit.zdb_id: 435652-4
    SSG: 11
    Location Call Number Limitation Availability
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  • 6
    Online Resource
    Online Resource
    SAGE Publications ; 2021
    In:  Journal of Information Science Vol. 47, No. 5 ( 2021-10), p. 609-614
    In: Journal of Information Science, SAGE Publications, Vol. 47, No. 5 ( 2021-10), p. 609-614
    Abstract: Dividing papers based on their numbers of citations into several groups constitutes one of the most common research practices in bibliometrics and beyond. However, existing dividing methods are both arbitrary and subject to bias. This article proposes a novel approach to partition highly, medium and lowly cited publications based on their citation distribution. We utilise the whole Web of Science (WoS) dataset to demonstrate how to apply this approach to scholarly datasets and examine the robustness of our algorithm in each of the six disciplines under the WoS dataset. The codes that underlie the algorithm are available online.
    Type of Medium: Online Resource
    ISSN: 0165-5515 , 1741-6485
    RVK:
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 439125-1
    detail.hit.zdb_id: 2025062-9
    SSG: 24,1
    Location Call Number Limitation Availability
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  • 7
    In: Journal of Information Science, SAGE Publications
    Abstract: Scholar performance evaluation is extremely important in research assessment decisions, such as funding allocation, academic rankings, and academic promotion. In this article, we propose the institution Q model (IQ) and its two variants (IQ-2 and IQ-3), which aim to evaluate the individual-level research ability to publish high-quality scientific papers. Specifically, our models integrate scientists’ institutions, countries and collaborators as valuable prior information and jointly evaluate the research ability of scientists from different institutions. To estimate model parameters and hidden variables defined in our models, we propose a generic BBVI-EM algorithm. To test the effectiveness of our models, we examine their performance on the synthetic data and the empirical data (17,750/26,992 scientists in the computer science/physics field). We find that our models can more accurately quantify the research ability of scientists and institutions and more effectively predict scientists’ scientific impact (the h-index and total citations) than the Q model and common machine learning models. In conclusion, our models are effective evaluation and prediction tools for quantifying research ability and predicting the scientific impact, and the BBVI-EM algorithm is an effective variational inference algorithm. This study makes a theoretical contribution to broaden the idea of incorporating the academic environment into scientific evaluation.
    Type of Medium: Online Resource
    ISSN: 0165-5515 , 1741-6485
    RVK:
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 439125-1
    detail.hit.zdb_id: 2025062-9
    SSG: 24,1
    Location Call Number Limitation Availability
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