Dynamic network analytics for recommending scientific collaborators

Huang, Lu; Chen, Xiang; Zhang, Yi; Zhu, Yihe; Li, Suyi; Ni, Xingxing

doi:10.1007/s11192-021-04164-x

Cited by 11 publications

(2 citation statements)

References 68 publications

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“…Recently, the tech mining community also expressed great passion to elaborate cutting-edge artificial intelligence (AI) and data analytical techniques, from word embedding (Zhang et al, 2018a ), steaming data analytics (Zhang et al, 2017 ), to heterogeneous and dynamics network analytics (Huang et al, 2021 ; Zhang et al, 2022b ), and graph representation learning (Choi et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Tech mining: a revisit and navigation

Porter,

Zhang,

Newman

2024

Front. Res. Metr. Anal.

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This mini-review arrays the pertinent tools and purposes of “Tech Mining” – shorthand for empirical analyses of Science, Technology and Innovation (ST&I) data. The intent is to introduce the range of tools, and show how they can complement each other. Tech Mining aims to generate powerful intelligence to help manage R&D and innovation processes. We offer a 5-part array to help relate the analytical elements. An overview of a case study of Hybrid and Electric Vehicles illustrates the complexities involved and the potential to generate valuable “intel.”

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Section: Introductionmentioning

confidence: 99%

Tech mining: a revisit and navigation

Porter,

Zhang,

Newman

2024

Front. Res. Metr. Anal.

Self Cite

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“…A recommendation system consists of a set of tools and techniques to provide useful suggestions to the users for decision-making (Raza and Ding, 2022). In the scholarly domain, numerous recommendation systems have been developed with the focus to find relevant research articles (Bulut et al ., 2018, 2020; El Alaoui et al ., 2021; Zhu et al ., 2021), scientific collaborators (Alshareef et al ., 2018; Huang et al ., 2021; Makarov et al ., 2018; Rodrigues et al ., 2018; Wu et al ., 2020), citations (Färber et al ., 2018; Guilarte et al ., 2019; Jeong et al ., 2020; Wang et al ., 2020; Xie et al ., 2021) and journals/conferences (Ghosal et al ., 2019; Jain et al ., 2018; Wang et al ., 2018). In addition, recommending relevant Q&A is an emerging research domain.…”

Section: Introductionmentioning

confidence: 99%

Contextual analysis of scholarly communications to identify the source of disinformation on digital scholarly platforms

Desai

Mehta

Rana

2023

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PurposeScholarly communications, particularly, questions and answers (Q&A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation.Design/methodology/approachTo identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis.FindingsThe model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation.Practical implicationsDifferent platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars.Originality/valueThe existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.

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The drivers, features, and influence of first scientific collaboration among core scholars from Chinese library and information field

Peng,

Shi

2024

Asso for Info Science & Tech

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Collaboration among scholars in scientific research is increasingly common, making it important to address how to recommend suitable collaborators, especially for their first cooperation. To address this issue, this study focuses on 1487 core scholars in the field of library and information science in China, and then analyzes the impact of academic differences between these scholars in their first collaboration by using the propensity matching score method. It uncovers potential driving factors for scholars to reach first collaborations, including similar research productivity, contrasting academic influence, aligned research directions, and distinct research focuses. Then, the distribution of features of three types of first cooperation demonstrates that if one or both partners publish the first paper in this collaboration, the collaborative relationship tends to be more enduring and stable. In addition, the subsequent collaboration of scholars and the change in their academic differences are related to the initial academic differences between the two parties in the first collaboration. These patterns can be used to improve the accuracy and effectiveness of the scholar recommendation mechanism, hence promoting research collaboration and knowledge exchange.

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Dynamic network analytics for recommending scientific collaborators

Cited by 11 publications

References 68 publications

Tech mining: a revisit and navigation

Tech mining: a revisit and navigation

Contextual analysis of scholarly communications to identify the source of disinformation on digital scholarly platforms

The drivers, features, and influence of first scientific collaboration among core scholars from Chinese library and information field

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