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

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Complex Networks and Their Applications VII (COMPLEX NETWORKS 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 812))

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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.

This work grew out of portions of a research project that was carried out by the authors of this work in collaboration with Aman Panda and Duo Tao. We gratefully acknowledge their contributions. This work was supported in part by Carleton College. Comments are welcome.

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Author information

Authors and Affiliations

  1. Department of Psychology, Carleton College, Northfield, MN, 55057, USA

    Violet Brown, Maryam Hedayati & Julia Strand

  2. Department of Computer Science, Carleton College, Northfield, MN, 55057, USA

    Xi Chen, Maryam Hedayati, Camden Sikes, Tegan Wilson & David Liben-Nowell

Authors
  1. Violet Brown
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  2. Xi Chen
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  3. Maryam Hedayati
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  4. Camden Sikes
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  5. Julia Strand
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  6. Tegan Wilson
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  7. David Liben-Nowell
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Corresponding author

Correspondence to David Liben-Nowell .

Editor information

Editors and Affiliations

  1. Nokia Bell Labs, Cambridge, UK

    Luca Maria Aiello

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I UMR CNRS 6306 9, University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  4. Mathematical Institute, University of Oxford, Oxford, UK

    Renaud Lambiotte

  5. Department of Computer Science and Technology, The Computer Laboratory, University of Cambridge, Cambridge, UK

    Pietro Lió

  6. Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

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Brown, V. et al. (2019). Node Ordering for Rescalable Network Summarization (or, the Apparent Magic of Word Frequency and Age of Acquisition in the Lexicon). In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_6

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