Author
Liang
Bio: Liang is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 401 citations.
Papers
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01 Jan 2007
TL;DR: In this article, R-and AR-andAR-core are compared: R and AR-core, and AR and AR, respectively, in terms of their respective performance.
Abstract: 把 R-andAR 索引基于我们介绍并且学习的 h 索引打的基础。这些新索引消除一些 h 索引的劣势，特别当他们与 h 索引在联合被使用时。R 索引测量 h-core 的引证紧张，当 AR 去时进一步的一个步骤并且花出版物的很长时间进报道。这允许能实际上随着时间的过去增加并且减少的一个索引。我们建议对(h， AR ) 作为为研究评估的有意义的指示物。我们进一步证明尽最大努力描绘 h 索引的一种关系是法律模型。
402 citations
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TL;DR: The findings indicate that the h index is better than other indicators considered (total citation count, citations per paper, and total paper count) in predicting future scientific achievement.
Abstract: Bibliometric measures of individual scientific achievement are of particular interest if they can be used to predict future achievement. Here we report results of an empirical study of the predictive power of the h index compared with other indicators. Our findings indicate that the h index is better than other indicators considered (total citation count, citations per paper, and total paper count) in predicting future scientific achievement. We discuss reasons for the superiority of the h index.
899 citations
TL;DR: This contribution presents a comprehensive review on the h-index and related indicators field, studying their main advantages, drawbacks and the main applications that can be found in the literature.
748 citations
01 Aug 2020
TL;DR: SciSci has revealed choices and trade-offs that scientists face as they advance both their own careers and the scientific horizon, and offers a deep quantitative understanding of the relational structure between scientists, institutions, and ideas, which facilitates the identification of fundamental mechanisms responsible for scientific discovery.
Abstract: The rapid development of digital libraries and the proliferation of scholarly big data have created an unprecedented opportunity to explore scientific production and reward at scale. Fueled by the data exploration and computational advances in digital libraries, the science of science is an emerging multidisciplinary field that aims to quantify patterns for scientific relationships and dependencies, and how scientific progress emerges from the scholarly big data. In this tutorial, we will provide an overview of the science of science, including major topics on scientific careers, scientific collaborations and scientific ideas. We will also discuss its historical context, the state-of-art models and exciting discoveries, and promising future directions for participants interested in mining scholarly big data.
579 citations
TL;DR: The historical development of scientometrics, sources of citation data, citation metrics and the “laws” of scientometry, normalisation, journal impact factors and other journal metrics, visualising and mapping science, evaluation and policy, and future developments are considered.
560 citations
TL;DR: A family of H-indices are obtained that can be used to measure a node's importance and it is proved that the convergence to coreness can be guaranteed even under an asynchronous updating process, allowing a decentralized local method of calculating a nodes' coreness in large-scale evolving networks.
Abstract: Identifying influential nodes in dynamical processes is crucial in understanding network structure and function. Degree, H-index and coreness are widely used metrics, but previously treated as unrelated. Here we show their relation by constructing an operator , in terms of which degree, H-index and coreness are the initial, intermediate and steady states of the sequences, respectively. We obtain a family of H-indices that can be used to measure a node's importance. We also prove that the convergence to coreness can be guaranteed even under an asynchronous updating process, allowing a decentralized local method of calculating a node's coreness in large-scale evolving networks. Numerical analyses of the susceptible-infected-removed spreading dynamics on disparate real networks suggest that the H-index is a good tradeoff that in many cases can better quantify node influence than either degree or coreness.
486 citations