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@InProceedings{DiasValdPetrNona:2018:GrSpFi,
               author = "Dias, Markus Diego Sampaio da Silva and Valdivia, Paola and 
                         Petronetto, Fabiano and Nonato, Luis Gustavo",
          affiliation = "{Universidade de S{\~a}o Paulo} and {Universidade de S{\~a}o 
                         Paulo} and {Universidade Federal do Esp{\'{\i}}rito Santo} and 
                         {Universidade de S{\~a}o Paulo}",
                title = "Graph Spectral Filtering for Network Simplification",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "network, graph signal processing, spectral filtering, network 
                         simplification, visualization.",
             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.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "Paper ID 70.pdf",
        urlaccessdate = "2020, Dec. 04"
}


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