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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3RPA7N2
Repositorysid.inpe.br/sibgrapi/2018/09.03.19.21
Last Update2018:09.03.19.21.15 administrator
Metadatasid.inpe.br/sibgrapi/2018/09.03.19.21.15
Metadata Last Update2020:02.19.03.10.44 administrator
Citation KeyDiasValdPetrNona:2018:GrSpFi
TitleGraph Spectral Filtering for Network Simplification
FormatOn-line
Year2018
DateOct. 29 - Nov. 1, 2018
Access Date2021, Jan. 19
Number of Files1
Size5806 KiB
Context area
Author1 Dias, Markus Diego Sampaio da Silva
2 Valdivia, Paola
3 Petronetto, Fabiano
4 Nonato, Luis Gustavo
Affiliation1 Universidade de São Paulo
2 Universidade de São Paulo
3 Universidade Federal do Espírito Santo
4 Universidade de São Paulo
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressmarkusdiegossd@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2018-09-03 19:21:15 :: markusdiegossd@gmail.com -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsnetwork, graph signal processing, spectral filtering, network simplification, visualization.
AbstractVisualization is an important tool in the analysis and understanding of networks and their content. However, visualization tools face major challenges when dealing with large networks, mainly due to visual clutter. In this context, network simplification has been a main alternative to handle massive networks, reducing complexity while preserving relevant patterns of the network structure and content. In this paper we propose a methodology that rely on Graph Signal Processing theory to filter multivariate data associated to network nodes, assisting and enhancing network simplification and visualization tasks. The simplification process takes into account both topological and multivariate data associated to network nodes to create a hierarchical representation of the network. The effectiveness of the proposed methodology is assessed through a comprehensive set of quantitative evaluation and comparisons, which gauge the impact of the proposed filtering process in the simplification and visualization tasks.
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data URLhttp://urlib.net/rep/8JMKD3MGPAW/3RPA7N2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RPA7N2
Languageen
Target FilePaper ID 70.pdf
User Groupmarkusdiegossd@gmail.com
Visibilityshown
Update Permissionnot transferred
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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