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      Large-scale analysis of delayed recognition using sleeping beauty and the prince

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          Abstract

          Delayed recognition in which innovative discoveries are re-evaluated after a long period has significant implications for scientific progress. The quantitative method to detect delayed recognition is described as the pair of Sleeping Beauty (SB) and its Prince (PR), where SB refers to citation bursts and its PR triggers SB’s awakeness calculated based on their citation history. This research provides the methods to extract valid and large SB–PR pairs from a comprehensive Scopus dataset and analyses how PR discovers SB. We prove that the proposed method can extract long-sleep and large-scale SB and its PR best covers the previous multi-disciplinary pairs, which enables to observe delayed recognition. Besides, we show that the high-impact SB–PR pairs extracted by the proposed method are more likely to be located in the same field. This indicates that a hidden SB that your research can awaken may exist closer than you think. On the other hand, although SB–PR pairs are fat-tailed in Beauty Coefficient and more likely to integrate separate fields compared to ordinary citations, it is not possible to predict which citation leads to awake SB using the rarity of citation. There is no easy way to limit the areas where SB–PR pairs occur or detect it early, suggesting that researchers and administrators need to focus on a variety of areas. This research provides comprehensive knowledge about the development of scientific findings that will be evaluated over time.

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          Most cited references53

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          Fast unfolding of communities in large networks

          Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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            Deep learning in neural networks: An overview

            In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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              From Louvain to Leiden: guaranteeing well-connected communities

              Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees.
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                Author and article information

                Contributors
                miura@ipr-ctr.t.u-tokyo.ac.jp
                asatani@ipr-ctr.t.u-tokyo.ac.jp
                isakata@ipr-ctr.t.u-tokyo.ac.jp
                Journal
                Appl Netw Sci
                Appl Netw Sci
                Applied Network Science
                Springer International Publishing (Cham )
                2364-8228
                30 June 2021
                30 June 2021
                2021
                : 6
                : 1
                : 48
                Affiliations
                GRID grid.26999.3d, ISNI 0000 0001 2151 536X, Department of Technology Management for Innovation, School of Engineering, , The University of Tokyo, ; 7-3-1 Faculty of Engineering Bldg 3, Hongo, Bunkyo, Tokyo, 113-8656 Japan
                Author information
                http://orcid.org/0000-0002-3409-3044
                https://orcid.org/0000-0003-3595-8940
                https://orcid.org/0000-0001-5881-3790
                Article
                389
                10.1007/s41109-021-00389-0
                8242290
                34226873
                b0071fad-f797-4ed0-b1d0-b783750d67c9
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 February 2021
                : 12 June 2021
                Categories
                Research
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                © The Author(s) 2021

                bibliometrics,citation network,delayed recognition,sleeping beauty,prince,interdisciplinary fusion,production of knowledge

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