Academic rising star prediction via scholar’s evaluation model and machine learning techniques

Nie, Yubing; Zhu, Yifan; Lin, Qika; Zhang, Sifan; Shi, Pengfei; Niu, Zhendong

doi:10.1007/s11192-019-03131-x

Cited by 33 publications

(9 citation statements)

References 36 publications

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“…Additionally, from the perspective of meteorological teaching, how to organize knowledge efficiently to establish accurate push for professionals and students in this field is worthy of further study (Tarus et al , ; Wan & Niu, ). Similarly, how to mine new knowledge in the measurement of scientific literature, and even predict new hotspots is also an important direction of meteorological knowledge services (Tarus et al , ; Nie et al , ; Yousif et al , ).…”

Section: Discussion: Challenges and Future Of Social Weathermentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

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Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing-driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary.Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing-based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological 62 | ZHU et al.

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Section: Discussion: Challenges and Future Of Social Weathermentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

Self Cite

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“…Analysis and mining based on big data technology have been implemented on these data and the analyses of researchers have been a hot topic. The researcher data analysis tasks including collaborator recommendation [19], [20], collaboration sustainability prediction [2], [21], reviewer recommendation [22], [23], expert finding [24], [25], advisoradvisee discovery [26], [27], academic influence prediction [19], [28], etc. Mainstream works focus on mining the various academic characteristics and community graph properties of researchers, then learn task-specific researcher representations for various tasks.…”

Section: A Researcher Data Miningmentioning

confidence: 99%

RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training

Qiao,

Fu,

Wang

et al. 2021

Preprint

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With the growth of the academic engines, the mining and analysis acquisition of massive researcher data, such as collaborator recommendation and researcher retrieval, has become indispensable. It can improve the quality of services and intelligence of academic engines. Most of the existing studies for researcher data mining focus on a single task for a particular application scenario and learning a task-specific model, which is usually unable to transfer to out-of-scope tasks. For example, the collaborator recommendation models maybe not be suitable to solve the researcher classification problem. The pre-training technology provides a generalized and sharing model to capture valuable information from enormous unlabeled data. The model can accomplish multiple downstream tasks via a few finetuning steps. Although pre-training models have achieved great success in many domains, existing models cannot be directly applied to researcher data, which is heterogeneous and contains textual attributes and graph-structured social relationships. In this paper, we propose a multi-task self-supervised learning-based researcher data pre-training model named RPT, which is efficient to accomplish multiple researcher data mining tasks. Specifically, we divide the researchers' data into semantic document sets and community graph. We design the hierarchical Transformer and the local community encoder to capture information from the two categories of data, respectively. Then, we propose three self-supervised learning objectives to train the whole model. For RPT's main task, we leverage contrastive learning to discriminate whether these captured two kinds of information belong to the same researcher. In addition, two auxiliary tasks, named hierarchical masked language model and community relation prediction for extracting semantic and community information, are integrated to improve pre-training. Finally, we also propose two transfer modes of RPT for fine-tuning in different scenarios. We conduct extensive experiments to evaluate RPT, results on three downstream tasks verify the effectiveness of pre-training for researcher data mining.

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“…[40] used RF model to predict references in the field of environmental modeling. [41] extracted the author feature, time feature and other features, compared the K-Nearest Neighbor(KNN) algorithm, Random Forest(RF), gradient lifting decision tree(GDBT), extreme gradient lifting(XGB) and support vector machine(SVM) to verify the stability and outstanding performance of k-nearest neighbor algorithm.…”

Section: Citation Counts Predictionmentioning

confidence: 99%

Utilizing Citation Network Structure to Predict Citation Counts: A Deep Learning Approach

Zhao

2020

Preprint

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With the advancement of science and technology, the number of academic papers published in the world each year has increased almost exponentially. While a large number of research papers highlight the prosperity of science and technology, they also give rise to some problems. As we all know, academic papers are the most intuitive embodiment of the research results of scholars, which can reflect the level of researchers. It is also the evaluation standard for decision-making such as promotion and allocation of funds. Therefore, how to measure the quality of an academic paper is very important. The most common standard for measuring academic papers is the number of citation counts of papers, because this indicator is widely used in the evaluation of scientific publications, and it also serves as the basis for many other indicators (such as the h-index). Therefore, it is very important to be able to accurately predict the citation counts of academic papers.This paper proposes an end-to-end deep learning network, DeepCCP, which combines the effect of information cascade and looks at the citation counts prediction problem from the perspective of information cascade prediction. DeepCCP directly uses the citation network formed in the early stage of the paper as the input, and the output is the citation counts of the corresponding paper after a period of time. DeepCCP only uses the structure and temporal information of the citation network, and does not require other additional information, but it can still achieve outstanding performance. According to experiments on 6 real data sets, DeepCCP is superior to the state-of-the-art methods in terms of the accuracy of citation *

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Academic rising star prediction via scholar’s evaluation model and machine learning techniques

Cited by 33 publications

References 36 publications

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training

Utilizing Citation Network Structure to Predict Citation Counts: A Deep Learning Approach

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