Analysis of the Neighborhood Pattern Similarity Measure for the Role Extraction Problem

Marchand, Melissa;Gallivan, Kyle;Huang, Wen;Van Dooren, Paul
(2021) SIAM Journal on Mathematics of Data Science — Vol. 3, n° 2, p. 736-757 (2021)

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Authors
  • Marchand, MelissaFlorida State University, Tallahassee, USA
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  • Gallivan, KyleFlorida State University, Tallahassee, USA
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  • Huang, WenXiamen University, China
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  • Van Dooren, Paulorcid-logoUCLouvain
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Abstract
In this paper we analyze an indirect approach, called the Neighborhood Pattern Similarity approach, to solve the so-called role extraction problem of a large-scale graph. The method is based on the preliminary construction of a node similarity matrix, which allows in a second stage to group together, with an appropriate clustering technique, the nodes that are assigned to have the same role. The analysis builds on the notion of ideal graphs where all nodes with the same role are also structurally equivalent.
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Marchand, M., Gallivan, K., Huang, W., & Van Dooren, P. (2021). Analysis of the Neighborhood Pattern Similarity Measure for the Role Extraction Problem. SIAM Journal on Mathematics of Data Science, 3(2), 736-757. https://doi.org/10.1137/20m1358785 (Original work published 2021)