Using random walks to generate associations between objects.

Measuring similarities between objects based on their attributes has been an important problem in many disciplines. Object-attribute associations can be depicted as links on a bipartite graph. A similarity measure can be thought as a unipartite projection of this bipartite graph. The most widely use...

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Autores principales:	Muhammed A Yildirim, Michele Coscia
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bec936a0d7d84c10a6ecb3f7446bc103
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id	oai:doaj.org-article:bec936a0d7d84c10a6ecb3f7446bc103
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spelling	oai:doaj.org-article:bec936a0d7d84c10a6ecb3f7446bc1032021-11-25T06:03:17ZUsing random walks to generate associations between objects.1932-620310.1371/journal.pone.0104813https://doaj.org/article/bec936a0d7d84c10a6ecb3f7446bc1032014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25153830/?tool=EBIhttps://doaj.org/toc/1932-6203Measuring similarities between objects based on their attributes has been an important problem in many disciplines. Object-attribute associations can be depicted as links on a bipartite graph. A similarity measure can be thought as a unipartite projection of this bipartite graph. The most widely used bipartite projection techniques make assumptions that are not often fulfilled in real life systems, or have the focus on the bipartite connections more than on the unipartite connections. Here, we define a new similarity measure that utilizes a practical procedure to extract unipartite graphs without making a priori assumptions about underlying distributions. Our similarity measure captures the relatedness between two objects via the likelihood of a random walker passing through these nodes sequentially on the bipartite graph. An important aspect of the method is that it is robust to heterogeneous bipartite structures and it controls for the transitivity similarity, avoiding the creation of unrealistic homogeneous degree distributions in the resulting unipartite graphs. We test this method using real world examples and compare the obtained results with alternative similarity measures, by validating the actual and orthogonal relations between the entities.Muhammed A YildirimMichele CosciaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 8, p e104813 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Muhammed A Yildirim Michele Coscia Using random walks to generate associations between objects.
description	Measuring similarities between objects based on their attributes has been an important problem in many disciplines. Object-attribute associations can be depicted as links on a bipartite graph. A similarity measure can be thought as a unipartite projection of this bipartite graph. The most widely used bipartite projection techniques make assumptions that are not often fulfilled in real life systems, or have the focus on the bipartite connections more than on the unipartite connections. Here, we define a new similarity measure that utilizes a practical procedure to extract unipartite graphs without making a priori assumptions about underlying distributions. Our similarity measure captures the relatedness between two objects via the likelihood of a random walker passing through these nodes sequentially on the bipartite graph. An important aspect of the method is that it is robust to heterogeneous bipartite structures and it controls for the transitivity similarity, avoiding the creation of unrealistic homogeneous degree distributions in the resulting unipartite graphs. We test this method using real world examples and compare the obtained results with alternative similarity measures, by validating the actual and orthogonal relations between the entities.
format	article
author	Muhammed A Yildirim Michele Coscia
author_facet	Muhammed A Yildirim Michele Coscia
author_sort	Muhammed A Yildirim
title	Using random walks to generate associations between objects.
title_short	Using random walks to generate associations between objects.
title_full	Using random walks to generate associations between objects.
title_fullStr	Using random walks to generate associations between objects.
title_full_unstemmed	Using random walks to generate associations between objects.
title_sort	using random walks to generate associations between objects.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/bec936a0d7d84c10a6ecb3f7446bc103
work_keys_str_mv	AT muhammedayildirim usingrandomwalkstogenerateassociationsbetweenobjects AT michelecoscia usingrandomwalkstogenerateassociationsbetweenobjects
_version_	1718414244618174464

Using random walks to generate associations between objects.

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