Towards a Social Trust Based Measure of Scientific Productivity

Gayen, Avijit; Bhavsar, Maitry; Chandra, Joydeep

doi:10.1145/3110025.3110082

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Moreover, to further investigate the innovation speed of preprints with different citation impacts, we categorized the AI preprints into high-impact and low-impact AI preprints based on their citation counts. Specifically, all the AI preprints were ranked in a descending order by citation counts, and the top 20% of them were considered as high-impact, last 40% as low-impact (Gayen, Bhavsar & Chandra, 2017). Besides, according to whether a preprint has been officially published in a journal or a conference, we also classify the AI preprints into two categories, i.e., AI preprints with official version and AI preprints without official version.…”

Section: Classifying Ai Preprints and Their Authorsmentioning

confidence: 99%

“…Besides, similar with the AI preprints, we also divided the AI authors into high-influential and lowinfluential AI authors according to their influence, which was represented by the value of "InfluentialCitationCount" from Semantic scholar. In particular, all the AI authors were ranked in a descending order by author influence, and the top 20% of them were marked as high-influential, last 40% as lowinfluential (Gayen, Bhavsar & Chandra, 2017).…”

Section: Classifying Ai Preprints and Their Authorsmentioning

confidence: 99%

See 1 more Smart Citation

The pace of artificial intelligence innovations: Speed, talent, and trial-and-error

Tang

Ding

et al. 2020

Journal of Informetrics

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Section: Classifying Ai Preprints and Their Authorsmentioning

confidence: 99%

Section: Classifying Ai Preprints and Their Authorsmentioning

confidence: 99%

The pace of artificial intelligence innovations: Speed, talent, and trial-and-error

Tang

Ding

et al. 2020

Journal of Informetrics

View full text Add to dashboard Cite

“…Trust-based complex networks have been used in a plethora of applications [3] such as recommender systems [4], productivity assessments [5], and security mechanisms [6]. Establishments of various trust network models in collaboration networks [5] [7], social ego networks [8], and likewise, has opened various evolutionary multi-layered complex networks to model trust between distinct or similar entities. The first leap towards modeling such trust networks came with modeling of the scale-free [9] www or the world wide web [10] [11].…”

Section: Introductionmentioning

confidence: 99%

Discrete nature of relational trust networks: A multilayered model for healthcare systems

Chakraborty¹,

Gayen²

2020

Preprint

Self Cite

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Medical Trust-Network is one of the most promising fields of study in network science. Establishment of trust within medical entities ensures better treatment and increases better medical facilities. The word 'Trust' signifies a very important behavioral aspect between any human entities, especially among doctors and patients. To represent such relationships Trust Network Models are built to express the interactions between human entities within such networks. Though the idea of a Trust-Network has traditionally been one of the major areas of research, yet the concept of a medical trust network model is relatively a new domain. In this paper, we introduce an overall multilayered Trust Network to represent the entire healthcare architecture. More specifically our model is based on an evolutionary graph system with a discrete relationship between the three most important entities of any healthcare system, namely -- Doctors, Departments, and Hospitals. Observations indicate that based on our model, the medical healthcare system is a multilayered model unlike a feed-forward model as indicated by previous studies.

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“…Trust-based complex networks have been used in a plethora of applications [4] such as recommender systems [5], productivity assessments [6], and security mechanisms [7]. Establishments of various trust network models in collaboration networks [8] [9], social ego networks [9], and likewise, has opened various evolutionary multi-layered complex networks to model trust between distinct or similar entities. The first leap towards modeling such trust networks came with modeling of the scale-free [10] www or the world wide web [11] [12].…”

Section: Introductionmentioning

confidence: 99%

A Multilayered Healthcare Architecture Using Relational Trust Network

Chakraborty¹,

Gayen²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Medical Trust-Network is one of the most promising fields of study in network science. Establishment of trust within medical entities ensures better treatment and increases better medical facilities. The word ‘Trust’ signifies a very important behavioral aspect between any human entities, especially among doctors and patients. To represent such relationships Trust Network Models are built to express the interactions between human entities within such networks. Though the idea of a Trust-Network has traditionally been one of the major areas of research, yet the concept of a medical trust network model is relatively a new domain. In this paper, we introduce an overall multilayered Trust Network to represent the entire healthcare architecture. More specifically our model is based on an evolutionary graph system with a discrete relationship between the three most important entities of any healthcare system, namely – Doctors, Departments, and Hospitals. Observations indicate that based on our model, the medical healthcare system is a multilayered model unlike a feed-forward model as indicated by previous studies.

show abstract

Towards a Social Trust Based Measure of Scientific Productivity

Cited by 5 publications

References 23 publications

The pace of artificial intelligence innovations: Speed, talent, and trial-and-error

The pace of artificial intelligence innovations: Speed, talent, and trial-and-error

Discrete nature of relational trust networks: A multilayered model for healthcare systems

A Multilayered Healthcare Architecture Using Relational Trust Network

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