Search (4 results, page 1 of 1)

  • × author_ss:"Liu, X."
  • × theme_ss:"Informetrie"
  1. Jiang, Z.; Liu, X.; Chen, Y.: Recovering uncaptured citations in a scholarly network : a two-step citation analysis to estimate publication importance (2016) 0.02 
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    Abstract
    The citation relationships between publications, which are significant for assessing the importance of scholarly components within a network, have been used for various scientific applications. Missing citation metadata in scholarly databases, however, create problems for classical citation-based ranking algorithms and challenge the performance of citation-based retrieval systems. In this research, we utilize a two-step citation analysis method to investigate the importance of publications for which citation information is partially missing. First, we calculate the importance of the author and then use his importance to estimate the publication importance for some selected articles. To evaluate this method, we designed a simulation experiment-"random citation-missing"-to test the two-step citation analysis that we carried out with the Association for Computing Machinery (ACM) Digital Library (DL). In this experiment, we simulated different scenarios in a large-scale scientific digital library, from high-quality citation data, to very poor quality data, The results show that a two-step citation analysis can effectively uncover the importance of publications in different situations. More importantly, we found that the optimized impact from the importance of an author (first step) is exponentially increased when the quality of citation decreases. The findings from this study can further enhance citation-based publication-ranking algorithms for real-world applications.
    Date
    12. 6.2016 20:31:29
  2. Zhang, C.; Liu, X.; Xu, Y.(C.); Wang, Y.: Quality-structure index : a new metric to measure scientific journal influence (2011) 0.01 
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    Abstract
    An innovative model to measure the influence among scientific journals is developed in this study. This model is based on the path analysis of a journal citation network, and its output is a journal influence matrix that describes the directed influence among all journals. Based on this model, an index of journals' overall influence, the quality-structure index (QSI), is derived. Journal ranking based on QSI has the advantage of accounting for both intrinsic journal quality and the structural position of a journal in a citation network. The QSI also integrates the characteristics of two prevailing streams of journal-assessment measures: those based on bibliometric statistics to approximate intrinsic journal quality, such as the Journal Impact Factor, and those using a journal's structural position based on the PageRank-type of algorithm, such as the Eigenfactor score. Empirical results support our finding that the new index is significantly closer to scholars' subjective perception of journal influence than are the two aforementioned measures. In addition, the journal influence matrix offers a new way to measure two-way influences between any two academic journals, hence establishing a theoretical basis for future scientometrics studies to investigate the knowledge flow within and across research disciplines.
  3. Liu, X.; Kaza, S.; Zhang, P.; Chen, H.: Determining inventor status and its effect on knowledge diffusion : a study on nanotechnology literature from China, Russia, and India (2011) 0.01 
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    Abstract
    In an increasingly global research landscape, it is important to identify the most prolific researchers in various institutions and their influence on the diffusion of knowledge. Knowledge diffusion within institutions is influenced by not just the status of individual researchers but also the collaborative culture that determines status. There are various methods to measure individual status, but few studies have compared them or explored the possible effects of different cultures on the status measures. In this article, we examine knowledge diffusion within science and technology-oriented research organizations. Using social network analysis metrics to measure individual status in large-scale coauthorship networks, we studied an individual's impact on the recombination of knowledge to produce innovation in nanotechnology. Data from the most productive and high-impact institutions in China (Chinese Academy of Sciences), Russia (Russian Academy of Sciences), and India (Indian Institutes of Technology) were used. We found that boundary-spanning individuals influenced knowledge diffusion in all countries. However, our results also indicate that cultural and institutional differences may influence knowledge diffusion.
  4. Liu, X.; Zhang, J.; Guo, C.: Full-text citation analysis : a new method to enhance scholarly networks (2013) 0.01 
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    Abstract
    In this article, we use innovative full-text citation analysis along with supervised topic modeling and network-analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author-contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. To evaluate this work, we sampled 104 topics (labeled with keywords) in review papers. The cited publications of each review paper are assumed to be "important publications" for the target topic (keyword), and we use these cited publications to validate our topic-ranking result and to compare different publication-ranking lists. Evaluation results show that full-text citation and publication content prior topic distribution, along with the classical PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance, comparing with term frequency-inverted document frequency (tf-idf), language model, BM25, PageRank, and PageRank + language model (p < .001), for academic information retrieval (IR) systems.

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