A new method to construct co-author networks

Liu, Jie; Liu, Yunpeng; Ruan, Zichan; Fu, Guohong; Chen, Xiaowu; Sadiq, Rehan; Deng, Yong

doi:10.1016/j.physa.2014.10.006

Cited by 41 publications

(16 citation statements)

References 62 publications

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“…The DEMATEL structure and calculation steps are summarized and explained in the following sections (Yang and Tzeng 2011; Wu et al 2013; Liu et al 2015; Zhang et al 2015; Tsai 2016; Tsai et al 2016b; Zhou et al 2016). …”

Section: Methodsmentioning

confidence: 99%

An empirical research on evaluating banks’ credit assessment of corporate customers

Tsai

et al. 2016

SpringerPlus

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Background Under the rapid change of the global financial environment, the risk control of the credit granting is viewed as the foremost task to each bank. With the impact one by one from financial crisis and European debt crisis, the steady bank business is also facing the severe challenge. Banks approve the credits for their customers and then make money from the interest.Case presentation Credit granting is not only the primary job but also the main source of income. The quality of credit granting concerns not just the reclaims of creditor’s rights; it also affects the successful running of banks.Discussion and EvaluationTo enhance the reliability and usefulness of bank credit risk assessment, we first will delve in the facets and indexes in the bank credit risk assessment. Then, we will examine the different dimensions of cause–effect relationships and correlations in the assessment process. Finally, the study focuses on how to raise the functions and benefits of the bank credit risk assessment.ConclusionsIn those five credit risk evaluation dimensions, A “optional capability” and D “competitiveness” are of high relation and high prominence among those dimensions, influencing other items obviously. By actively focusing on these two dimensions and improving their credit risk assessment ability will solve the foremost problems and also solve other facets of credit risk assessment problems at the same time.

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Section: Methodsmentioning

confidence: 99%

An empirical research on evaluating banks’ credit assessment of corporate customers

Tsai

et al. 2016

SpringerPlus

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“…We use similar metrics in our work to understand the emerging HCD+D field. Liu et al (2015a) proposed a method based on the PageRank algorithm to evaluate the importance of authors in a co-author network; their method measures the influence of each authors' papers over time by their number of citations. Their focus was on determining the importance of authors in co-author networks to better evaluate the impact of a paper by calibrating the paper's influence over time.…”

Section: Social Network Analysismentioning

confidence: 99%

Co-author network analysis of human-centered design for development

Kramer

Gordon

et al. 2018

Des. Sci.

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Human-centered design (HCD) offers a systematic approach to innovation practice, driven by customer research and feedback throughout the design process. Within the community of engineers and researchers who engage in design for global development, interest in HCD has grown in the past decade. In this paper, we examine the human-centered design for development (HCD+D) academic community to better understand the interactions between researchers. By building and evaluating a co-authorship network from a dataset of HCD+D papers, in which the nodes are researchers and the connecting links are co-authorship relationships, we provide a decade-long benchmark to answer a variety of questions about collaboration patterns within this emerging field. Our analysis shows that most HCD+D authors publish few papers and are part of small, well-connected sub-communities. Influential authors that bridge separate communities are few. HCD+D is emerging from disparate disciplines and widely shared scholarship across disciplines continues to be developed. Influential authors in HCD+D play a large role in shaping HCD+D, yet there are few authors that are in a position to connect and influence collaborative research. Our analysis gives rise to several implications including an increased need for cross-disciplinary collaboration and the need for a stronger core of HCD+D practitioners.

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“…(1999). The PageRank considers the number and quality of links to a web page in order to determine how influential it is (Liu et al, 2014). Let T A be a web page and T i one of the web pages that connects to T A .…”

Section: Social Network Analysis Metricsmentioning

confidence: 99%

“…One of the most famous co-authorship networks is the mathematician Paul Erdos network, which has more than 500 co-authors and more than 1,400 published works. The role of Erdos as a collaborator was so significant in the field of mathematics that the Erdos number is set to measure the proximity to Erdos through network co-authorship (Liu et al, 2014). Anyone who has published with Erdos has an Erdos number equal to 1, those having a publication with a co-author Erdos have an Erdos number equal to 2, and so on (Newman, 2001c).…”

Section: Introductionmentioning

confidence: 99%

“…Kempe & Kleinberg (2005) also reported the emergence of many researches of co-authorship network analysis that try to identify the most influential authors in it. It is also observed that the works of Huang et al (2013) and Liu et al (2014) sought to study the co-authorship network to assess the status of an author in a particular field, and thus enhance the relations to get closer to the community core by identifying the most influential researchers.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Co-Authorship Network Among Cnpq’s Productivity Fellows in the Area of Industrial Engineering

Andrade

Rêgo

2017

Pesqui. Oper.

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ABSTRACT. In this article, we have built a co-authorship network among researchers with CNPQ grant in research productivity (PQ) in the area of Industrial Engineering and analyze which Social Network Analysis metrics impact their productivity level. Unlike other studies that mostly analyze unweighted networks, ours explored more broadly the network since the metrics were calculated in three ways: unweighted, including the edges weights and including the edges and nodes' attributes. Thus, the generated results are more precise and detailed since more information is obtained. We consider the h-index of the researchers as the nodes' attributes and measured the impact using Kendall correlation. We show that geographical distance is still a barrier to collaboration among PQs in this area and that collaboration with researchers with different levels of grant has the greatest impact in the level of the grant a researcher has.

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A new method to construct co-author networks

Cited by 41 publications

References 62 publications

An empirical research on evaluating banks’ credit assessment of corporate customers

An empirical research on evaluating banks’ credit assessment of corporate customers

Co-author network analysis of human-centered design for development

Exploring the Co-Authorship Network Among Cnpq’s Productivity Fellows in the Area of Industrial Engineering

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