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Node Ordering for Rescalable Network Summarization (or, the Apparent Magic of Word Frequency and Age of Acquisition in the Lexicon)

  • Violet Brown
  • Xi Chen
  • Maryam Hedayati
  • Camden Sikes
  • Julia Strand
  • Tegan Wilson
  • David Liben-Nowell
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

How can we “scale down” an n-node network G to a smaller network \(G'\), with \(k \ll n\) nodes, so that \(G'\) (approximately) maintains the important structural properties of G? There is a voluminous literature on many versions of this problem if k is given in advance, but one’s tolerance for approximation (and the resulting value of k) will vary. Here, then, we formulate a “rescalable” version of this approximation task for complex networks. Specifically, we propose a node ordering version of graph summarization: permute the nodes of G so that the subgraph induced by the first k nodes is a good size-k approximation of G, averaged over the full range of possible sizes k. We consider as a case study the phonological network of English words, and discover two natural word orders (word frequency and age of acquisition) that do a surprisingly good job of rescalably summarizing the lexicon.

Keywords

Network summarization Node ordering Phonological networks 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Violet Brown
    • 1
  • Xi Chen
    • 2
  • Maryam Hedayati
    • 1
    • 2
  • Camden Sikes
    • 2
  • Julia Strand
    • 1
  • Tegan Wilson
    • 2
  • David Liben-Nowell
    • 2
  1. 1.Department of PsychologyCarleton CollegeNorthfieldUSA
  2. 2.Department of Computer ScienceCarleton CollegeNorthfieldUSA

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