Search (13 results, page 1 of 1)

  • × author_ss:"Ding, Y."
  1. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.01
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    Date
    22. 1.2011 13:02:21
  2. Ding, Y.: Scholarly communication and bibliometrics : Part 1: The scholarly communication model: literature review (1998) 0.01
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    Date
    1. 3.1997 19:57:52
  3. Ding, Y.; Foo, S.: Ontology research and development : part 1 - a review of ontology generation (2002) 0.01
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  4. Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014) 0.01
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    Date
    22. 8.2014 16:52:04
  5. Ding, Y.; Chowdhury, G.C.; Foo, S.: Incorporating the results of co-word analyses to increase search variety for information retrieval (2000) 0.01
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    Date
    1. 4.2002 13:43:47
  6. Ding, Y.: Visualization of intellectual structure in information retrieval : author cocitation analysis (1998) 0.01
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    Date
    1. 3.1997 19:57:52
    Source
    International forum on information and documentation. 23(1998) no.1, S.25-36
  7. Yan, E.; Ding, Y.: Discovering author impact : a PageRank perspective (2011) 0.01
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    Source
    Information processing and management. 47(2011) no.1, S.125-134
  8. Lin, N.; Li, D.; Ding, Y.; He, B.; Qin, Z.; Tang, J.; Li, J.; Dong, T.: ¬The dynamic features of Delicious, Flickr, and YouTube (2012) 0.00
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    Abstract
    This article investigates the dynamic features of social tagging vocabularies in Delicious, Flickr, and YouTube from 2003 to 2008. Three algorithms are designed to study the macro- and micro-tag growth as well as the dynamics of taggers' activities, respectively. Moreover, we propose a Tagger Tag Resource Latent Dirichlet Allocation (TTR-LDA) model to explore the evolution of topics emerging from those social vocabularies. Our results show that (a) at the macro level, tag growth in all the three tagging systems obeys power law distribution with exponents lower than 1; at the micro level, the tag growth of popular resources in all three tagging systems follows a similar power law distribution; (b) the exponents of tag growth vary in different evolving stages of resources; (c) the growth of number of taggers associated with different popular resources presents a feature of convergence over time; (d) the active level of taggers has a positive correlation with the macro-tag growth of different tagging systems; and (e) some topics evolve into several subtopics over time while others experience relatively stable stages in which their contents do not change much, and certain groups of taggers continue their interests in them.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.139-162
  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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    Abstract
    This study explores the similarity among six types of scholarly networks aggregated at the institution level, including bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks. Cosine distance is chosen to measure the similarities among the six networks. The authors found that topical networks and coauthorship networks have the lowest similarity; cocitation networks and citation networks have high similarity; bibliographic coupling networks and cocitation networks have high similarity; and coword networks and topical networks have high similarity. In addition, through multidimensional scaling, two dimensions can be identified among the six networks: Dimension 1 can be interpreted as citation-based versus noncitation-based, and Dimension 2 can be interpreted as social versus cognitive. The authors recommend the use of hybrid or heterogeneous networks to study research interaction and scholarly communications.
  10. Zhang, C.; Bu, Y.; Ding, Y.; Xu, J.: Understanding scientific collaboration : homophily, transitivity, and preferential attachment (2018) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 69(2018) no.1, S.72-86
  11. 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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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.1, S.185-204
  12. 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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    Date
    1. 5.2014 18:00:59
  13. Bu, Y.; Ding, Y.; Xu, J.; Liang, X.; Gao, G.; Zhao, Y.: Understanding success through the diversity of collaborators and the milestone of career (2018) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 69(2018) no.1, S.87-97