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Block dense weighted networks with augmented degree correction

Published online by Cambridge University Press:  14 September 2022

Benjamin Leinwand Open the ORCID record for Benjamin Leinwand [Opens in a new window]  and
Vladas Pipiras Open the ORCID record for Vladas Pipiras [Opens in a new window]
Benjamin Leinwand
Affiliation:
Stevens Institute of Technology, Hoboken, NJ, USA
Vladas Pipiras*
Affiliation:
University of North Carolina at Chapel Hill, NC, USA
*
*Corresponding author. Email: pipiras@email.unc.edu
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Abstract

Dense networks with weighted connections often exhibit a community-like structure, where although most nodes are connected to each other, different patterns of edge weights may emerge depending on each node’s community membership. We propose a new framework for generating and estimating dense weighted networks with potentially different connectivity patterns across different communities. The proposed model relies on a particular class of functions which map individual node characteristics to the edges connecting those nodes, allowing for flexibility while requiring a small number of parameters relative to the number of edges. By leveraging the estimation techniques, we also develop a bootstrap methodology for generating new networks on the same set of vertices, which may be useful in circumstances where multiple data sets cannot be collected. Performance of these methods is analyzed in theory, simulations, and real data.

Keywords

dense networksweighted networksdegree corrected block modelbootstrapcommunity detection
Type
Research Article
Information
Network Science , Volume 10 , Issue 3 , September 2022 , pp. 301 - 321
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

Action Editor: Fernando Vega-Redondo

†

The second author was supported in part by the NSF grant DMS-1712966.

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