Search (29 results, page 2 of 2)

  • × author_ss:"Ding, Y."
  • × year_i:[2010 TO 2020}
  1. Bu, Y.; Ding, Y.; Liang, X.; Murray, D.S.: Understanding persistent scientific collaboration (2018) 0.00 
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    Abstract
    Common sense suggests that persistence is key to success. In academia, successful researchers have been found more likely to be persistent in publishing, but little attention has been given to how persistence in maintaining collaborative relationships affects career success. This paper proposes a new bibliometric understanding of persistence that considers the prominent role of collaboration in contemporary science. Using this perspective, we analyze the relationship between persistent collaboration and publication quality along several dimensions: degree of transdisciplinarity, difference in coauthor's scientific age and their scientific impact, and research-team size. Contrary to traditional wisdom, our results show that persistent scientific collaboration does not always result in high-quality papers. We find that the most persistent transdisciplinary collaboration tends to output high-impact publications, and that those coauthors with diverse scientific impact or scientific ages benefit from persistent collaboration more than homogeneous compositions. We also find that researchers persistently working in large groups tend to publish lower-impact papers. These results contradict the colloquial understanding of collaboration in academia and paint a more nuanced picture of how persistent scientific collaboration relates to success, a picture that can provide valuable insights to researchers, funding agencies, policy makers, and mentor-mentee program directors. Moreover, the methodology in this study showcases a feasible approach to measure persistent collaboration.
    Type
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  2. Li, R.; Chambers, T.; Ding, Y.; Zhang, G.; Meng, L.: Patent citation analysis : calculating science linkage based on citing motivation (2014) 0.00 
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    Abstract
    Science linkage is a widely used patent bibliometric indicator to measure patent linkage to scientific research based on the frequency of citations to scientific papers within the patent. Science linkage is also regarded as noisy because the subject of patent citation behavior varies from inventors/applicants to examiners. In order to identify and ultimately reduce this noise, we analyzed the different citing motivations of examiners and inventors/applicants. We built 4 hypotheses based upon our study of patent law, the unique economic nature of a patent, and a patent citation's market effect. To test our hypotheses, we conducted an expert survey based on our science linkage calculation in the domain of catalyst from U.S. patent data (2006-2009) over 3 types of citations: self-citation by inventor/applicant, non-self-citation by inventor/applicant, and citation by examiner. According to our results, evaluated by domain experts, we conclude that the non-self-citation by inventor/applicant is quite noisy and cannot indicate science linkage and that self-citation by inventor/applicant, although limited, is more appropriate for understanding science linkage.
    Type
    a
  3. Li, D.; Luo, Z.; Ding, Y.; Tang, J.; Sun, G.G.-Z.; Dai, X.; Du, J.; Zhang, J.; Kong, S.: User-level microblogging recommendation incorporating social influence (2017) 0.00 
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    Abstract
    With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.
    Type
    a
  4. Tan, L.K.-W.; Na, J.-C.; Ding, Y.: Influence diffusion detection using the influence style (INFUSE) model (2015) 0.00 
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    Abstract
    Blogs are readily available sources of opinions and sentiments that in turn could influence the opinions of the blog readers. Previous studies have attempted to infer influence from blog features, but they have ignored the possible influence styles that describe the different ways in which influence is exerted. We propose a novel approach to analyzing bloggers' influence styles and using the influence styles as features to improve the performance of influence diffusion detection among linked bloggers. The proposed influence style (INFUSE) model describes bloggers' influence through their engagement style, persuasion style, and persona. Methods used include similarity analysis to detect the creating-sharing aspect of engagement style, subjectivity analysis to measure persuasion style, and sentiment analysis to identify persona style. We further extend the INFUSE model to detect influence diffusion among linked bloggers based on the bloggers' influence styles. The INFUSE model performed well with an average F1 score of 76% compared with the in-degree and sentiment-value baseline approaches. Previous studies have focused on the existence of influence among linked bloggers in detecting influence diffusion, but our INFUSE model is shown to provide a fine-grained description of the manner in which influence is diffused based on the bloggers' influence styles.
    Type
    a
  5. Huang, Y.; Bu, Y.; Ding, Y.; Lu, W.: From zero to one : a perspective on citing (2019) 0.00 
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    Type
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  6. Sugimoto, C.R.; Li, D.; Russell, T.G.; Finlay, S.C.; Ding, Y.: ¬The shifting sands of disciplinary development : analyzing North American Library and Information Science dissertations using latent Dirichlet allocation (2011) 0.00 
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    Abstract
    This work identifies changes in dominant topics in library and information science (LIS) over time, by analyzing the 3,121 doctoral dissertations completed between 1930 and 2009 at North American Library and Information Science programs. The authors utilize latent Dirichlet allocation (LDA) to identify latent topics diachronically and to identify representative dissertations of those topics. The findings indicate that the main topics in LIS have changed substantially from those in the initial period (1930-1969) to the present (2000-2009). However, some themes occurred in multiple periods, representing core areas of the field: library history occurred in the first two periods; citation analysis in the second and third periods; and information-seeking behavior in the fourth and last period. Two topics occurred in three of the five periods: information retrieval and information use. One of the notable changes in the topics was the diminishing use of the word library (and related terms). This has implications for the provision of doctoral education in LIS. This work is compared to other earlier analyses and provides validation for the use of LDA in topic analysis of a discipline.
    Type
    a
  7. Li, D.; Ding, Y.; Sugimoto, C.; He, B.; Tang, J.; Yan, E.; Lin, N.; Qin, Z.; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model (2011) 0.00 
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    Abstract
    The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems.
    Type
    a
  8. Ding, Y.; Jacob, E.K.; Fried, M.; Toma, I.; Yan, E.; Foo, S.; Milojevicacute, S.: Upper tag ontology for integrating social tagging data (2010) 0.00 
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  9. Ding, Y.; Yan, E.: Scholarly network similarities : how bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other (2012) 0.00 
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