ArnetMiner: Deep Mining for Academic Social Networks

Jie, Tang; Li, Juanzi

doi:10.1007/978-3-031-79462-9_5

Cited by 36 publications

(47 citation statements)

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“…Dataset & Evaluation methods To test and evaluate the performances of PreF1, PoF1, and PoF2, we apply an academic publication dataset 6 [24]. It includes academic articles and their citation relationships until 2019-05-05, which are extracted from DBLP, MAG, and AMiner.…”

Section: Methodsmentioning

confidence: 99%

Analyzing Performances of Three Context-Aware Collaborator Recommendation Algorithms in Terms of Accuracy and Time Efficiency

Li¹,

Abel²,

Negre³

2021

Lecture Notes in Business Information Processing

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de

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Section: Methodsmentioning

confidence: 99%

Analyzing Performances of Three Context-Aware Collaborator Recommendation Algorithms in Terms of Accuracy and Time Efficiency

Li¹,

Abel²,

Negre³

2021

Lecture Notes in Business Information Processing

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“…Song et al [6] attempted to detect the knowledge structure of bioinformatics by applying LDA to a large set of bioinformatics full-text articles for topic model generation. Author-Conference-Topic (ACT) [7] model (an extension of LDA that can incorporate the paper, author, and conference into the topic distribution simultaneously.) was adopted by Heo et al [8] to study the field of bioinformatics from the perspective of key phrases, authors, and journals.…”

Section: Text Mining Applied To Bioinformatics Bibliometricsmentioning

confidence: 99%

Visualizing the knowledge structure and evolution of bioinformatics

Wang

Zhang

2022

BMC Bioinformatics

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Background Bioinformatics has gained much attention as a fast growing interdisciplinary field. Several attempts have been conducted to explore the field of bioinformatics by bibliometric analysis, however, such works did not elucidate the role of visualization in analysis, nor focus on the relationship between sub-topics of bioinformatics. Results First, the hotspot of bioinformatics has moderately shifted from traditional molecular biology to omics research, and the computational method has also shifted from mathematical model to data mining and machine learning. Second, DNA-related topics are bridge topics in bioinformatics research. These topics gradually connect various sub-topics that are relatively independent at first. Third, only a small part of topics we have obtained involves a number of computational methods, and the other topics focus more on biological aspects. Fourth, the proportion of computing-related topics hit a trough in the 1980s. During this period, the use of traditional calculation methods such as mathematical model declined in a large proportion while the new calculation methods such as machine learning have not been applied in a large scale. This proportion began to increase gradually after the 1990s. Fifth, although the proportion of computing-related topics is only slightly higher than the original, the connection between other topics and computing-related topics has become closer, which means the support of computational methods is becoming increasingly important for the research of bioinformatics. Conclusions The results of our analysis imply that research on bioinformatics is becoming more diversified and the ranking of computational methods in bioinformatics research is also gradually improving.

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“…13) [19] was analyzed to verify the research. The details of its construction are described in [20]. This set contains data on 5,354,309 scientific publications and 48,227,950 citations of these publications, collected from databases DBLP [21], ACM [22], Microsoft Academic Graph [23], and others.…”

Section: The Assessment Of Scientific Productivitymentioning

confidence: 99%

The Scientific Productivity of Collective Subjects Based on the Time-Weighted Pagerank Method with Citation Intensity

Kuchansky¹,

Biloshchytskyi²,

Andrashko³

et al. 2022

Preprint

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This study aims to estimate the scientific productivity of collective subjects. The objective is to build a method for evaluating scientific productivity that allows calculating productivity, including for new collective subjects with a small citation network—the paper purposes the Time-Weighted PageRank method with citation intensity (TWPR-CI). The Citation Network Dataset (ver. 13) has been analyzed to verify the method. The dataset includes more than 5 million scientific publications and 48 million citations. There have been allocated four classes of collective subjects (more than 27,000 collective subjects in total). For each class, scientific productivity estimates from 2000 to 2021 were calculated using the PageRank, Time-Weighted PageRank, and TWPR-CI methods. It is shown that the advantage of the TWPR-CI method is the higher sensitivity of the scientific productivity estimates for new collective subjects on average during the first ten years of observation. At the same time, the assessment of scientific productivity for other collective subjects according to this method is stable. However, the small citation network of the new collective subjects does not allow an adequate assessment of scientific productivity during the first years of its operation. Therefore, the TWPR-CI method can be used to assess the scientific productivity of collective subjects, in particular the productivity of new ones.

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ArnetMiner: Deep Mining for Academic Social Networks

Cited by 36 publications

References 0 publications

Analyzing Performances of Three Context-Aware Collaborator Recommendation Algorithms in Terms of Accuracy and Time Efficiency

Analyzing Performances of Three Context-Aware Collaborator Recommendation Algorithms in Terms of Accuracy and Time Efficiency

Visualizing the knowledge structure and evolution of bioinformatics

The Scientific Productivity of Collective Subjects Based on the Time-Weighted Pagerank Method with Citation Intensity

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