Leveraging citation influences for Modeling scientific documents

Qian, Yue; Liu, Yu; Xu, Xiujuan; Sheng, Quan Z.

doi:10.1007/s11280-020-00796-w

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…Zhu et al introduced j-index [ 37 ] to model the topic level academic influence according to the novelty of each article and its contribution to the cited article. A novel method is proposed to quantify the higher-order citation influence of publications to quantify and visualize citation flows among disciplines and to assess their degree of interdisciplinarity considering both direct and indirect citations [ 38 ]. Hu et al construct time-aware weighted graphs [ 39 ] to quantify the importance of links established at different times to fuse the rich information in a mutual reinforcement ranking framework to rank the future influence of multiobjects simultaneously.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

Guan

et al. 2021

Computational Intelligence and Neuroscience

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Computer science discipline includes many research fields, which mutually influence and promote each other’s development. This poses two great challenges of predicting the research topics of each research field. One is how to model fine-grained topic representation of a research field. The other is how to model research topic of different fields and keep the semantic consistency of research topics when learning the scientific influence context from other related fields. Unfortunately, the existing research topic prediction approaches cannot handle these two challenges. To solve these problems, we employ multiple different Recurrent Neural Network chains which model research topics of different fields and propose a research topic prediction model based on spatial attention and semantic consistency-based scientific influence modeling. Spatial attention is employed in field topic representation which can selectively extract the attributes from the field topics to distinguish the importance of field topic attributes. Semantic consistency-based scientific influence modeling maps research topics of different fields to a unified semantic space to obtain the scientific influence context of other related fields. Extensive experiment results on five related research fields in the computer science (CS) discipline show that the proposed model is superior to the most advanced methods and achieves good topic prediction performance.

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Section: Related Workmentioning

confidence: 99%

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

Guan

et al. 2021

Computational Intelligence and Neuroscience

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“…Citations are used to understand how familiar a particular article is among peers in the field, and in terms of citations, India had an outstanding record with 3084 citations in total, followed by the USA with 1773 citations (information obtained from the data). However, care should be taken to analyse and understand the nuances of citations since trends are influenced by many parameters, and citation distribution remains highly skewed [53,54]. Also, the highest citation (21.96) was recorded for Iran, despite the country producing only institutions involved in research, and if this value is more than average, it illustrates considerable interest.…”

Section: Top Ten Publishing Countriesmentioning

confidence: 99%

Global Research Trends in Engineered Soil Development through Stabilisation: Scientific Production and Thematic Breakthrough Analysis

Ravindran,

Bahrami,

Mahesh

et al. 2023

Buildings

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Soil, a naturally occurring resource, is increasingly used as a construction material. Stabilisation strengthens soil, which is weak as an engineering material. Stabilising soil changes its physical qualities, enhancing its strength. Soil stabilisation increases the shear strength and load-bearing capacity. Soil stabilisation refers to any endeavour to change natural soil for engineering purposes using physical, chemical, mechanical, or biological methods, or a mix of these. Strengthening road pavements includes improving the load-bearing capacity, tensile strength, and performance of unstable subsoils, sands, and waste materials. Due to market demands and scientific advances, the number of soil-stabilising additives has increased. These innovative stabilisers include reinforcing fibres, calcium chloride, sodium chloride, and cross-linking water-based styrene acrylic polymers, which are geopolymers that boost the load-bearing capacity and tensile strength of soil. Many materials are being explored for soil stabilisation. In this article, the authors investigated the direction of soil stabilisation research. Scientometric analysis identifies stabilisation challenges and research trends in the field. This study analysed research patterns by countries, authors, institutions, keywords, and journals from 1959 to 2023; in 2021, 150 articles were published, which was the highest number in a year. Citations peaked at 3084 in 2022. With 253 publications and 3084 citations, India was the most productive country. Iran and France published the fewest, 34 and 33, respectively. The Islamic Azad University and the National Institute of Technology had the fewest published articles with 17 articles. This work can help track soil stabilisation research and will serve as an information document for future research.

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“…Zhu et al introduced j‐index 48 to model the topic level academic influence according to the novelty of each article and its contribution to the cited article. A novel method is proposed to quantify the higher‐order citation influence of publications to quantify and visualize citation flows among disciplines, and to assess their degree of interdisciplinarity considering both direct and indirect citations 49 . Hu and Cao construct time‐aware weighted graphs 50 to quantify the importance of links established at different times to fuse the rich information in a mutual reinforcement ranking framework to rank the future influence of multiobjects simultaneously.…”

Section: Related Workmentioning

confidence: 99%

“…A novel method is proposed to quantify the higherorder citation influence of publications to quantify and visualize citation flows among disciplines, and to assess their degree of interdisciplinarity considering both direct and indirect citations. 49 Hu and Cao construct time-aware weighted graphs 50 to quantify the importance of links established at different times to fuse the rich information in a mutual reinforcement ranking framework to rank the future influence of multiobjects simultaneously. The above methods do not use the scientific influence to explore the topic prediction of future research trends, only a small number of studies have explored this topic.…”

Section: Scientific Influence Modelingmentioning

confidence: 99%

A scientific research topic trend prediction model based on multi‐LSTM and graph convolutional network

Xue

et al. 2022

Int J of Intelligent Sys

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In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Accurately and effectively predicting the trends of future research topics can help researchers discover future research hotspots.However, due to the increasingly close correlation between various research themes, there is a certain dependency relationship between a large number of research themes. Viewing a single research theme in isolation and using traditional sequence problem processing methods cannot effectively explore the spatial dependencies between these research themes. To simultaneously capture the spatial dependencies and temporal changes between research topics, we propose a deep neural network-based research topic hotness prediction algorithm, a spatiotemporal convolutional network model. Our model combines a graph convolutional neural network (GCN) and Temporal Convolutional Network (TCN), specifically, GCNs are used to learn the spatial dependencies of research topics a and use space dependence to strengthen spatial characteristics. TCN is used to learn the dynamics of research topics' trends. Optimization is based on the calculation of weighted losses based on time distance. Compared with the current mainstream sequence prediction models and similar spatiotemporal models on the paper datasets, experiments show that, in research topic prediction tasks, our model can effectively capture spatiotemporal relationships and the predictions outperform state-of-art baselines.

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Leveraging citation influences for Modeling scientific documents

Cited by 4 publications

References 35 publications

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

Global Research Trends in Engineered Soil Development through Stabilisation: Scientific Production and Thematic Breakthrough Analysis

A scientific research topic trend prediction model based on multi‐LSTM and graph convolutional network

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