Search (44 results, page 1 of 3)

  • × author_ss:"Ding, Y."
  1. 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) 0.11 
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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.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1313-1326
  2. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.08 
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    Abstract
    This article aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information retrieval (IR) was selected as a test field and data from 1956-2008 were collected from Web of Science. Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal component analysis was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures
    Date
    22. 1.2011 13:02:21
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.236-245
  3. Yan, E.; Ding, Y.; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks (2011) 0.05 
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    Abstract
    Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.467-477
  4. 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) 0.03 
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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.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1849-1866
  5. Lu, C.; Zhang, Y.; Ahn, Y.-Y.; Ding, Y.; Zhang, C.; Ma, D.: Co-contributorship network and division of labor in individual scientific collaborations (2020) 0.03 
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    Abstract
    Collaborations are pervasive in current science. Collaborations have been studied and encouraged in many disciplines. However, little is known about how a team really functions from the detailed division of labor within. In this research, we investigate the patterns of scientific collaboration and division of labor within individual scholarly articles by analyzing their co-contributorship networks. Co-contributorship networks are constructed by performing the one-mode projection of the author-task bipartite networks obtained from 138,787 articles published in PLoS journals. Given an article, we define 3 types of contributors: Specialists, Team-players, and Versatiles. Specialists are those who contribute to all their tasks alone; team-players are those who contribute to every task with other collaborators; and versatiles are those who do both. We find that team-players are the majority and they tend to contribute to the 5 most common tasks as expected, such as "data analysis" and "performing experiments." The specialists and versatiles are more prevalent than expected by our designed 2 null models. Versatiles tend to be senior authors associated with funding and supervision. Specialists are associated with 2 contrasting roles: the supervising role as team leaders or marginal and specialized contributors.
    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.10, S.1162-1178
  6. He, B.; Ding, Y.; Ni, C.: Mining enriched contextual information of scientific collaboration : a meso perspective (2011) 0.03 
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    Abstract
    Studying scientific collaboration using coauthorship networks has attracted much attention in recent years. How and in what context two authors collaborate remain among the major questions. Previous studies, however, have focused on either exploring the global topology of coauthorship networks (macro perspective) or ranking the impact of individual authors (micro perspective). Neither of them has provided information on the context of the collaboration between two specific authors, which may potentially imply rich socioeconomic, disciplinary, and institutional information on collaboration. Different from the macro perspective and micro perspective, this article proposes a novel method (meso perspective) to analyze scientific collaboration, in which a contextual subgraph is extracted as the unit of analysis. A contextual subgraph is defined as a small subgraph of a large-scale coauthorship network that captures relationship and context between two coauthors. This method is applied to the field of library and information science. Topological properties of all the subgraphs in four time spans are investigated, including size, average degree, clustering coefficient, and network centralization. Results show that contextual subgprahs capture useful contextual information on two authors' collaboration.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.5, S.831-845
  7. Yan, E.; Ding, Y.: Applying centrality measures to impact analysis : a coauthorship network analysis (2009) 0.03 
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    Abstract
    Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988-2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.10, S.2107-2118
  8. Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009) 0.03 
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    Abstract
    This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.11, S.2229-2243
  9. Ding, Y.: Topic-based PageRank on author cocitation networks (2011) 0.03 
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    Abstract
    Ranking authors is vital for identifying a researcher's impact and standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h-index, PageRank, and weighted PageRank), but most of them are topic-independent. This paper proposes topic-dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The author-conference-topic (ACT) model was used to extract topic distribution of individual authors. Two ways for combining the ACT model with the PageRank algorithm are proposed: simple combination (I_PR) or using a topic distribution as a weighted vector for PageRank (PR_t). Information retrieval was chosen as the test field and representative authors for different topics at different time phases were identified. Principal component analysis (PCA) was applied to analyze the ranking difference between I_PR and PR_t.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.449-466
  10. 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
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.9, S.1820-1833
  11. Ding, Y.; Chowdhury, G.C.; Foo, S.: Bibliometric cartography of information retrieval research by using co-word analysis (2001) 0.00 
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    Source
    Information processing and management. 37(2001) no.6, S.817-842
  12. Ding, Y.; Chowdhury, G.C.; Foo, S.: Incorporating the results of co-word analyses to increase search variety for information retrieval (2000) 0.00 
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    Source
    Journal of information science. 26(2000) no.6, S.429-451
  13. Ding, Y.: Visualization of intellectual structure in information retrieval : author cocitation analysis (1998) 0.00 
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    Abstract
    Reports results of a cocitation analysis study from the international retrieval research field from 1987 to 1997. Data was taken from Social SciSearch, via Dialog, and the top 40 authors were submitted to author cocitation analysis to yield the intellectual structure of information retrieval. The resulting multidimensional scaling map revealed: identifiable author groups for information retrieval; location of these groups with respect to each other; extend of centrality and peripherality of authors within groups, proximities of authors within groups and across group boundaries; and the meaning of the axes of the map. Factor analysis was used to reveal the extent of the authors' research areas and statistical routines included: ALSCAL; clustering analysis and factor analysis
    Source
    International forum on information and documentation. 23(1998) no.1, S.25-36
  14. Ding, Y.; Foo, S.: Ontology research and development : part 1 - a review of ontology generation (2002) 0.00 
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    Source
    Journal of information science. 28(2002) no.2, S.123-136
  15. Ding, Y.: ¬A review of ontologies with the Semantic Web in view (2001) 0.00 
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    Source
    Journal of information science. 27(2001) no.?, S.377-384
  16. Ding, Y.; Chowdhury, G.; Foo, S.: Organsising keywords in a Web search environment : a methodology based on co-word analysis (2000) 0.00 
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    Abstract
    The rapid development of the Internet and World Wide Web has caused some critical problem for information retrieval. Researchers have made several attempts to solve these problems. Thesauri and subject heading lists as traditional information retrieval tools have been criticised for their efficiency to tackle these newly emerging problems. This paper proposes an information retrieval tool generated by cocitation analysis, comprising keyword clusters with relationships based on the co-occurrences of keywords in the literature. Such a tool can play the role of an associative thesaurus that can provide information about the keywords in a domain that might be useful for information searching and query expansion
  17. 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.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.1, S.185-204
  18. Milojevic, S.; Sugimoto, C.R.; Yan, E.; Ding, Y.: ¬The cognitive structure of Library and Information Science : analysis of article title words (2011) 0.00 
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    Abstract
    This study comprises a suite of analyses of words in article titles in order to reveal the cognitive structure of Library and Information Science (LIS). The use of title words to elucidate the cognitive structure of LIS has been relatively neglected. The present study addresses this gap by performing (a) co-word analysis and hierarchical clustering, (b) multidimensional scaling, and (c) determination of trends in usage of terms. The study is based on 10,344 articles published between 1988 and 2007 in 16 LIS journals. Methodologically, novel aspects of this study are: (a) its large scale, (b) removal of non-specific title words based on the "word concentration" measure (c) identification of the most frequent terms that include both single words and phrases, and (d) presentation of the relative frequencies of terms using "heatmaps". Conceptually, our analysis reveals that LIS consists of three main branches: the traditionally recognized library-related and information-related branches, plus an equally distinct bibliometrics/scientometrics branch. The three branches focus on: libraries, information, and science, respectively. In addition, our study identifies substructures within each branch. We also tentatively identify "information seeking behavior" as a branch that is establishing itself separate from the three main branches. Furthermore, we find that cognitive concepts in LIS evolve continuously, with no stasis since 1992. The most rapid development occurred between 1998 and 2001, influenced by the increased focus on the Internet. The change in the cognitive landscape is found to be driven by the emergence of new information technologies, and the retirement of old ones.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.10, S.1933-1953
  19. Ding, Y.; Foo, S.: Ontology research and development : part 2 - a review of ontology mapping and evolving (2002) 0.00 
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    Source
    Journal of information science. 28(2002) no.3, S.375-388
  20. Yan, E.; Ding, Y.: Weighted citation : an indicator of an article's prestige (2010) 0.00 
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    Abstract
    The authors propose using the technique of weighted citation to measure an article's prestige. The technique allocates a different weight to each reference by taking into account the impact of citing journals and citation time intervals. Weightedcitation captures prestige, whereas citation counts capture popularity. They compare the value variances for popularity and prestige for articles published in the Journal of the American Society for Information Science and Technology from 1998 to 2007, and find that the majority have comparable status.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1635-1643

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