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		<identifier>8JMKD3MGPAW/3RPA7N2</identifier>
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		<citationkey>DiasValdPetrNona:2018:GrSpFi</citationkey>
		<title>Graph Spectral Filtering for Network Simplification</title>
		<format>On-line</format>
		<year>2018</year>
		<date>Oct. 29 - Nov. 1, 2018</date>
		<numberoffiles>1</numberoffiles>
		<size>5806 KiB</size>
		<author>Dias, Markus Diego Sampaio da Silva,</author>
		<author>Valdivia, Paola,</author>
		<author>Petronetto, Fabiano,</author>
		<author>Nonato, Luis Gustavo,</author>
		<affiliation>Universidade de São Paulo</affiliation>
		<affiliation>Universidade de São Paulo</affiliation>
		<affiliation>Universidade Federal do Espírito Santo</affiliation>
		<affiliation>Universidade de São Paulo</affiliation>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<e-mailaddress>markusdiegossd@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<documentstage>not transferred</documentstage>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<tertiarytype>Full Paper</tertiarytype>
		<keywords>network, graph signal processing, spectral filtering, network simplification, visualization.</keywords>
		<abstract>Visualization 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.</abstract>
		<language>en</language>
		<targetfile>Paper ID 70.pdf</targetfile>
		<usergroup>markusdiegossd@gmail.com</usergroup>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2018/09.03.19.21</url>
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