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Scholarly network comparisons

Scholarly network comparisons. Erjia Yan, Ying Ding, Cassidy Sugimoto. Backgrounds I. Motivation I. A higher level of research aggregate – the institution - is rarely studied An institution is a stable and representative unit to study the production, diffusion, and consumption of knowledge

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Scholarly network comparisons

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  1. Scholarly network comparisons Erjia Yan, Ying Ding, Cassidy Sugimoto

  2. Backgrounds I

  3. Motivation I • A higher level of research aggregate – the institution - is rarely studied • An institution is a stable and representative unit to study the production, diffusion, and consumption of knowledge • An institution is a distinct research entity which provides an opportunity for the combination of mappings from social, geographical, and cognitive perspectives.

  4. Backgrounds II

  5. Motivation II • With the advancement of social network analysis, several types of scholarly networks are introduced to bibliometrics, such as citation networks, bibliographic coupling networks, cocitation networks, and coauthorship networks • These networks have their own uses but currently we are unaware of the similarity among them

  6. Dataset • 59 journals indexed as the Information Science & Library Science category. • All document types published within these journals from January 1965 to February 2010 were downloaded for analysis. • Data were processed in two steps • To filter the dataset in order to create a local citation network between institutions • To identify unique institution names from the affiliation data

  7. Network size

  8. The construction of citation and coauthorship networks

  9. The construction of cocitation and bibliographic coupling networks

  10. The construction of topical networks • Author-Conference-Topic (ACT) Model (Tang et al., 2008) • Ten topics: • The topic similarity between two institutions can be calculated through cosine similarity • Sij is then the line value between institution i and institution j in the topical network

  11. Clustering and mapping methods • VOSviewer clustering and mapping (Waltman, Eck, & Noyons, 2010) technique is selected • It is developed based on Clauset, Newman, and Moore’s (2004) algorithm for weighted networks.

  12. Distance measurements • Cosine distance (CD)

  13. Distance measurements • Earth mover’s distance (EMD)

  14. Basic network characteristics

  15. Clustering results of top institutions

  16. bibliographic coupling network

  17. citation network

  18. cocitation network

  19. coauthorship network

  20. topical network

  21. CD and EMD for each pair of networks

  22. Ranking of network similarities CD EMD

  23. Hybrid networks • In order to capture both social and cognitive aspects of interactions of certain research aggregates, two types of networks, one from the social side and the other from the cognitive side, can be combined and thus forming a hybrid network. • By considering the network density, we suggest the following combinations: • Coauthorship network and citation network; • Bibliographic coupling network and cocitation network; and • Bibliographic coupling network and topical network.

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