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"Frontiers in Chemistry: Rising Stars 2022" is a collection of research articles and reviews that highlights the work of emerging researchers in the field of chemistry. The publication features a diverse range of topics, including analytical chemistry, medicinal chemistry, and organic chemistry. The articles cover various innovative research findings and insights, contributing to the current knowledge in chemistry and paving the way for future discoveries. This compilation provides a platform for these promising scientists to share their work and acknowledges their potential to become future leaders in the field. [Extracted from the article]

The field of expertise extraction utilizes published research enabling communities to highlight and identify the skills of researchers within specific scientific domains. This can be useful for evaluating research performance, and in the case of rising stars, in identifying top scientific talent. Previous research has harvested a range of publication indicators in an effort to identify expertise and talent. These include content indicators, citation metrics, and also the position of a researcher within a full collaboration network of scientists. The existing mechanism of expertise extraction utilizes all papers attributed to a scientific author, thereby potentially neglecting their specific or specialized expertise. Here we show that a tensor decomposition technique when applied to the problem addresses a number of useful problems. This includes better identification of individual expertise, as well as an integrated appraisal of an author's role in an extended scientific network. The technique will afford new analyses of knowledge production which consider specialisation and diversity as core elements for further analysis. More generally the tensor decomposition techniques presented in this paper can be applied to a range of scientometric problems where multi-modal data is encountered. [ABSTRACT FROM AUTHOR]

The prediction of rising stars is a challenging task in social networks. To this end, publicly available online social databases are considered to retrieve meaningful information for prediction of future trends. According to our knowledge, we are the first to identify the rising stars in review domain by applying machine leaning methods. More specifically, we predict rising reviewers in yelp review network. Metadata, Rrecency-frequency-activity, and temporal categories are considered, and various features are proposed for each type. In addition, two performance measures are developed to evaluate the rising stars. As a binary classification problem, three popular machine learning models, 10-fold cross validation and two newly designed yelp reviewer's datasets are used. The proposed framework for rising stars is evaluated using various experiments, such as impact of individual feature, category-wise and model-wise performance. As an outcome, we find that our model demonstrates promising accuracy and f-measure values. In addition, two rankings of top-10 rising reviewers are presented (using weighted score and evolution score), and these rankings are validated against their current status from yelp.com. [ABSTRACT FROM AUTHOR]

In the context of big scholarly data, various metrics and indicators have been widely applied to evaluate the impact of scholars from different perspectives, such as publication counts, citations, h -index, and their variants. However, these indicators have limited capacity in characterizing prospective impacts or achievements of scholars. To solve this problem, we propose the Academic Potential Index (API) to quantify scholar's academic potential. Furthermore, an algorithm is devised to calculate the value of API. It should be noted that API is a dynamic index throughout scholar's academic career. By applying API to rank scholars, we can identify scholars who show their academic potentials during the early academic careers. With extensive experiments conducted based on the Microsoft Academic Graph dataset, it can be found that the proposed index evaluates scholars' academic potentials effectively and captures the variation tendency of their academic impacts. Besides, we also apply this index to identify rising stars in academia. Experimental results show that the proposed API can achieve superior performance in identifying potential scholars compared with three baseline methods.

In many natural science domains, while collaboration is inevitably becoming a vital factor for success, cultivating new promising researchers is becoming a by-product of this process. Young researchers or doctorate candidates can grow up quickly by the tutor's instructions, advanced experimental environment and fierce competition among themselves. In an existing study, we explored the premise that collaboration with an expert in the field can lead to success in the field of computer science. Other fields in natural science may yield results similar to computer science domain, however, domains such as philosophy, where successful scholars, rather than working on scientific articles, prefer spending more time in book writing and delivering lectures and seminars, may produce different results. This study attempts to find the effect of collaboration of top-class researchers on the career of junior researchers of philosophy from a dataset extracted from Ebrary and Scopus ranging from 2000 to 2016. The results of the study depict a relationship between the co-authorship with a top researcher of the field and the success in terms of impact. Results demonstrate that authors who collaborated with highly influential authority authors at an early stage of their career happened to be less productive at initial stages and more productive in later stages as compared to authors who worked with authority authors in later stages. The later ones gained more visibility in terms of citations, with high sociability at later stages and more longevity. However, there is a big number of philosophers who worked independently and still found to be highly productive and successful. [ABSTRACT FROM AUTHOR]

Online social databases are rich sources to retrieve appropriate information that is subsequently analyzed for forthcoming trends prediction. In this paper, we identify rising stars in cricket domain by employing machine learning techniques. More precisely, we predict rising stars from batting as well as from bowling realms. For this intent, the concepts of co-players, team, and opposite teams are incorporated and distinct features along with their mathematical formulations are presented. For classification purpose, generative and discriminative machine learning algorithms are employed, and two models from each category are evaluated. As a proof of applicability, the proposed approach is validated experimentally while analyzing the impact of individual features. Besides, model and categorywise assessment is also performed. Employing cross validation, we demonstrate high accuracy for rising star prediction that is both robust and statistically significant. Finally, ranking lists of top ten rising cricketers based on weighted average, performance evolution, and rising star scores are compared with the international cricket council rankings.

Predicting future academic rising stars provides a useful reference for research communities, such as offering decision support to recruit young researchers in research institutes. Academic rising stars prediction is considered to be a classification or regression task in the field of machine learning. Traditional methods of building label information for this task are only based on the increment of citation count, which cannot adequately reflect the evolution of a scholar's academic influence. In this paper, we first propose a non-iterative hierarchical weighted evaluation model based on the quality of citing papers and the influence of co-authors. Second, we label each young scholar by the increment of the impact score from our evaluation model in the classification task, aiming at better describing the change of a scholar's impact from more angles. Finally, different groups of features that can determine if a scholar will be a rising star are extracted, and various classification models are utilized to fit the classification relationships. The experimental results on the ArnetMiner dataset verify the feasibility of the prediction task based on our label construction method. We also find that the venue features are the best indicators for rising stars prediction in our experiments. [ABSTRACT FROM AUTHOR]

In today's complex academic environment the process of performance evaluation of scholars is becoming increasingly difficult. Evaluation committees often need to search in several repositories in order to deliver their evaluation summary report for an individual. However, it is extremely difficult to infer performance indicators that pertain to the evolution and the dynamics of a scholar. In this paper we propose a novel computational methodology based on unsupervised machine learning that can act as an important tool at the hands of evaluation committees of individual scholars. The suggested methodology compiles a list of several key performance indicators (features) for each scholar and monitors them over time. All these indicators are used in a clustering framework which groups the scholars into categories by automatically discovering the optimal number of clusters using clustering validity metrics. A profile of each scholar can then be inferred through the labeling of the clusters with the used performance indicators. These labels can ultimately act as the main profile characteristics of the individuals that belong to that cluster. Our empirical analysis gives emphasis on the “rising stars” who demonstrate the biggest improvement over time across all of the key performance indicators (KPIs), and can also be employed for the profiling of scholar groups. [ABSTRACT FROM AUTHOR]

Young scholars in academia often seek to work in collaboration with top researchers in their field in pursuit of a successful career. While success in academia can be defined differently, everyone agrees that training with a well-known researcher can help lead to an efficacious career. This study aims to investigate whether collaborating with established scientists does, in fact, improve junior scholars’ chances of success. If not, what makes young scientists soar in their academic careers? We investigate this question by analyzing the effect of collaboration with a known-star on success of a young scholar. The results suggest that working with leading experts can lead to a successful career, but that it is not the only way. Researchers who were not fortunate enough to start their career with an elite researcher could still succeed through hard work and passion. These findings emerged from analyses of two discrete sets of well-known scholars on the career of newcomers, suggesting their strength and validity. [ABSTRACT FROM AUTHOR]