Local Pluralistic Homophily in Networks: A New Measure Based on Overlapping Communities

Fernando Barraza, Carlos Ernesto Ramirez Ovalle, Alejandro Fernández

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Pluralistic homophily is an important phenomenon in social network analysis as nodes tend to associate with others that share their same communities. In this work, we present the concept of local pluralistic homophily of a node in a network, along with a method to measure it. It is based on the assortativity index proposed by other authors. We analyze the distribution of local pluralistic homophily in different networks using publicly available datasets. We identify patterns of behavior of the proposed measure that relate to various structural and topological characteristics of a network. These findings are significant because they help better understand how pluralistic homophily affects communities. Furthermore, our results suggest possible applications of local pluralistic homophily in future research.

Original languageEnglish
Title of host publicationCloud Computing, Big Data and Emerging Topics - 11th Conference, JCC-BD and ET 2023, Proceedings
EditorsMarcelo Naiouf, Enzo Rucci, Franco Chichizola, Laura De Giusti
PublisherSpringer Science and Business Media Deutschland GmbH
Pages75-87
Number of pages13
ISBN (Print)9783031409417
DOIs
StatePublished - 2023
Event11th Conference on Cloud Computing, Big Data and Emerging Topics, JCC-BD and ET 2023 - La Plata, Argentina
Duration: 27 Jun 202329 Jun 2023

Publication series

NameCommunications in Computer and Information Science
Volume1828 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference11th Conference on Cloud Computing, Big Data and Emerging Topics, JCC-BD and ET 2023
Country/TerritoryArgentina
CityLa Plata
Period27/06/2329/06/23

Keywords

  • Local Pluralistic Homophily
  • Networks
  • Communities

Fingerprint

Dive into the research topics of 'Local Pluralistic Homophily in Networks: A New Measure Based on Overlapping Communities'. Together they form a unique fingerprint.

Cite this