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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

Scholarly network comparisons

Erjia Yan, Ying Ding, Cassidy Sugimoto

motivation 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
  • An institution is a distinct research entity which provides an opportunity for the combination of mappings from social, geographical, and cognitive perspectives.
motivation ii
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
dataset
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
the construction of topical networks
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
clustering and mapping methods
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.
distance measurement s
Distance measurements
  • Cosine distance (CD)
distance measurement s1
Distance measurements
  • Earth mover’s distance (EMD)
hybrid networks
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.