Cargando…
A novel method for assessing and measuring homophily in networks through second-order statistics
We present a new method for assessing and measuring homophily in networks whose nodes have categorical attributes, namely when the nodes of networks come partitioned into classes (colors). We probe this method in two different classes of networks: (i) protein–protein interaction (PPI) networks, wher...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9192693/ https://www.ncbi.nlm.nih.gov/pubmed/35697749 http://dx.doi.org/10.1038/s41598-022-12710-7 |
_version_ | 1784726298695827456 |
---|---|
author | Apollonio, Nicola Franciosa, Paolo G. Santoni, Daniele |
author_facet | Apollonio, Nicola Franciosa, Paolo G. Santoni, Daniele |
author_sort | Apollonio, Nicola |
collection | PubMed |
description | We present a new method for assessing and measuring homophily in networks whose nodes have categorical attributes, namely when the nodes of networks come partitioned into classes (colors). We probe this method in two different classes of networks: (i) protein–protein interaction (PPI) networks, where nodes correspond to proteins, partitioned according to their functional role, and edges represent functional interactions between proteins (ii) Pokec on-line social network, where nodes correspond to users, partitioned according to their age, and edges respresent friendship between users.Similarly to other classical and well consolidated approaches, our method compares the relative edge density of the subgraphs induced by each class with the corresponding expected relative edge density under a null model. The novelty of our approach consists in prescribing an endogenous null model, namely, the sample space of the null model is built on the input network itself. This allows us to give exact explicit expression for the [Formula: see text] -score of the relative edge density of each class as well as other related statistics. The [Formula: see text] -scores directly quantify the statistical significance of the observed homophily via Čebyšëv inequality. The expression of each [Formula: see text] -score is entered by the network structure through basic combinatorial invariant such as the number of subgraphs with two spanning edges. Each [Formula: see text] -score is computed in [Formula: see text] time for a network with n nodes and m edges. This leads to an overall efficient computational method for assesing homophily. We complement the analysis of homophily/heterophily by considering [Formula: see text] -scores of the number of isolated nodes in the subgraphs induced by each class, that are computed in O(nm) time. Theoretical results are then exploited to show that, as expected, both the analyzed network classes are significantly homophilic with respect to the considered node properties. |
format | Online Article Text |
id | pubmed-9192693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91926932022-06-15 A novel method for assessing and measuring homophily in networks through second-order statistics Apollonio, Nicola Franciosa, Paolo G. Santoni, Daniele Sci Rep Article We present a new method for assessing and measuring homophily in networks whose nodes have categorical attributes, namely when the nodes of networks come partitioned into classes (colors). We probe this method in two different classes of networks: (i) protein–protein interaction (PPI) networks, where nodes correspond to proteins, partitioned according to their functional role, and edges represent functional interactions between proteins (ii) Pokec on-line social network, where nodes correspond to users, partitioned according to their age, and edges respresent friendship between users.Similarly to other classical and well consolidated approaches, our method compares the relative edge density of the subgraphs induced by each class with the corresponding expected relative edge density under a null model. The novelty of our approach consists in prescribing an endogenous null model, namely, the sample space of the null model is built on the input network itself. This allows us to give exact explicit expression for the [Formula: see text] -score of the relative edge density of each class as well as other related statistics. The [Formula: see text] -scores directly quantify the statistical significance of the observed homophily via Čebyšëv inequality. The expression of each [Formula: see text] -score is entered by the network structure through basic combinatorial invariant such as the number of subgraphs with two spanning edges. Each [Formula: see text] -score is computed in [Formula: see text] time for a network with n nodes and m edges. This leads to an overall efficient computational method for assesing homophily. We complement the analysis of homophily/heterophily by considering [Formula: see text] -scores of the number of isolated nodes in the subgraphs induced by each class, that are computed in O(nm) time. Theoretical results are then exploited to show that, as expected, both the analyzed network classes are significantly homophilic with respect to the considered node properties. Nature Publishing Group UK 2022-06-13 /pmc/articles/PMC9192693/ /pubmed/35697749 http://dx.doi.org/10.1038/s41598-022-12710-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Apollonio, Nicola Franciosa, Paolo G. Santoni, Daniele A novel method for assessing and measuring homophily in networks through second-order statistics |
title | A novel method for assessing and measuring homophily in networks through second-order statistics |
title_full | A novel method for assessing and measuring homophily in networks through second-order statistics |
title_fullStr | A novel method for assessing and measuring homophily in networks through second-order statistics |
title_full_unstemmed | A novel method for assessing and measuring homophily in networks through second-order statistics |
title_short | A novel method for assessing and measuring homophily in networks through second-order statistics |
title_sort | novel method for assessing and measuring homophily in networks through second-order statistics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9192693/ https://www.ncbi.nlm.nih.gov/pubmed/35697749 http://dx.doi.org/10.1038/s41598-022-12710-7 |
work_keys_str_mv | AT apollonionicola anovelmethodforassessingandmeasuringhomophilyinnetworksthroughsecondorderstatistics AT franciosapaolog anovelmethodforassessingandmeasuringhomophilyinnetworksthroughsecondorderstatistics AT santonidaniele anovelmethodforassessingandmeasuringhomophilyinnetworksthroughsecondorderstatistics AT apollonionicola novelmethodforassessingandmeasuringhomophilyinnetworksthroughsecondorderstatistics AT franciosapaolog novelmethodforassessingandmeasuringhomophilyinnetworksthroughsecondorderstatistics AT santonidaniele novelmethodforassessingandmeasuringhomophilyinnetworksthroughsecondorderstatistics |