Skip to main content

Mapping Knowledge Domains (2004) / Chapter Skim
Currently Skimming:

The simultaneous evolution of author and paper networks
Pages 84-91

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 84...
... A 20-year data set of articles published in PNAS is used to validate the model in terms of major network properties of the interlinked coauthor and paper citation networks. The subsequent sections review related research on descriptive and process models of coauthor and paper citation networks, discuss desirable features and basic assumptions of the process model, validate the model by comparing simulated data to a 20-year ISIII PNAS data set, and discuss the influence of model parameters such as the aging of papers in terms of their power to attract citations, the number of topics, and the length of the chain of references that authors consider when making citations.
Read the entire page →
From page 85...
... For example, to understand how knowledge diffuses across authors via their papers at the same time that new authors and papers are accumulated, it is essential to model the coupled growth of both network structures. Process Model for Author-Paper Networks This section motivates the features and simplifying assumptions of a process model for the simultaneous growth of coauthor and paper citation networks as seen in citation databases like PNAS.
Read the entire page →
From page 86...
... A parameterized model was implemented in Java to simulate the simultaneous growth of interlinked coauthor networks and paper citation networks as described above. The model takes as input parameters that specify the number of authors and papers created in the initial year as well as the number of topics, the number of authors to be deleted per year, the number of papers an author produces per year, the number of papers cited by a new paper, the number of coauthors, the number of levels references are followed up, and the parameters of the aging function.
Read the entire page →
From page 87...
... Later, we examine the influence of aging, reference path length, and number of topics on the structure of interlinked coauthor and paper citation networks. Model Validation To validate the TARL model, a 20-year (1982-2001)
Read the entire page →
From page 88...
... For paper citation net works, we do not report the value for the characteristic path length as it reflects the time duration of the sample but little about the structure of the network. Based on these values, the PNAS data set can be classified as a medium-sized data set that has a similar average node degree sky, path length 1, cluster coefficient C, and power law exponent By to the networks previously examined.
Read the entire page →
From page 89...
... , path length 1, cluster coefficient C, and power law exponent by Network n (k) I C Coauthorship networks LANL MEDLINE SPI RES NCSTRL Mathematics Neuroscience PNAS Paper citation networks 151 PhysRev PNAS SIM 52,909 9.7 1,520,251 18.1 56,627 1.73 11,994 3.59 70,975 3.9 209,293 11.5 105,915 8.97 783,339 8.57 24,296 14.5 45,120 3.53 37,114 2.13 5.9 0.43 4.6 0.066 4.0 0.726 9.7 0.496 9.5 0.59 6 0.76 5.89 0.399 0.081 0.074 Values for the first four coauthorship networks are taken from refs.
Read the entire page →
From page 90...
... Although the simulation does not fit the PNAS data any better than the power law with exponential tail, it does provide a process model for why this functional relation applies. Very highly cited papers are more rare in the PNAS and simulated data sets than predicted by a power law because of the bias toward citing recent papers.
Read the entire page →
From page 91...
... There are fewer papers that receive a large number of citations than is predicted by a power law, because the bias toward citing recent papers offsets the rich-get-richer effect that generates a power law relation. It is difficult for a well cited paper to continue to receive additional citations as it ages.
Read the entire page →

Key Terms

  • power law
  • citation networks
  • pnas data
  • path length
  • clustering coefficient

  • citation network
  • law exponent
  • process model
  • citation links
  • recent papers

  • model parameters
  • reference path
  • received citations
  • cluster coefficient
  • citation distribution

  • citations received
  • randomly selected
  • aging function
  • papers cited
  • process models

  • simulated data
  • highly cited
  • cited papers
  • randomly assigned
  • network structures

  • cite papers
  • law distribution
  • pnas paper
  • coauthorship networks
  • tarl model


    • This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
    More information on Chapter Skim is available.