Two-mode relational similarities

Bibliographic Data

ID18930509
AuthorsOmar Lizardo (0000-0002-5405-3007, University of California, Los Angeles, corresponding author)
Year2024
Volume76
Pages34-41
Publication date2024-01-01
Open AccessYes
TypeARTICLE
VenueSocial Networks (JOURNAL)
Journal identifiersISSN: 0378-8733 • E-ISSN: 1879-2111
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.socnet.2023.06.002
OpenAlexW4383892134
LanguageEN
Citations received3
References cited11

In a previous paper, Kovacs (2010) proposed a generalized relational similarity measure based on iterated correlations of entities in a network calibrated by their relational similarity to other entities. Here I show that, in the case of two-mode network data, Kovacs’s approach can be simplified and generalized similarities calculated non-iteratively. The basic idea is to rely on initial similarities calculated from transforming the two-mode data into one-mode projections using the familiar duality approach due to Breiger (1974). I refer to this as two-mode relational similarities and show, using the Southern Women’s data and data from Senate voting in the 112th U.S. Congress, that it yields results substantively indistinguishable from Kovacs’s iterative strategy

Data mining · Iterated function · Political science · Pure mathematics · Relational database · Relational model · Voting · Complex Network Analysis Techniques · Computational Drug Discovery Methods · Computer Science · Economic and Technological Innovation · Mathematics · Artificial Intelligence · Theoretical Computer Science

Tools

Open DOIOpen Access