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@InProceedings{DiasMaDiPeSiNo:2017:HiNeSi,
               author = "Dias, Markus Diego Sampaio da Silva and Mansour, Moussa R. and 
                         Dias, Fabio and Petronetto, Fabiano and Silva, Cl{\'a}udio T. and 
                         Nonato, Luis Gustavo",
          affiliation = "{Universidade de S{\~a}o Paulo} and {Universidade de S{\~a}o 
                         Paulo} and {Universidade de S{\~a}o Paulo} and {Universidade 
                         Federal do Esp{\'{\i}}rito Santo} and {New York University} and 
                         {Universidade de S{\~a}o Paulo}",
                title = "A Hierarchical Network Simplification Via Non-Negative Matrix 
                         Factorization",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "graph, matching, simplification, non-negative matrix 
                         factorization.",
             abstract = "Visualization tools play an important part in assisting analysts 
                         in the understanding of networks and underlying phenomena. However 
                         these tasks can be hindered by visual clutter. 
                         Simplification/decimation schemes have been a main alternative in 
                         this context. Nevertheless, network simplification methods have 
                         not been properly evaluated w.r.t. their effectiveness in reducing 
                         complexity while preserving relevant structures and content. 
                         Moreover, most simplification techniques only consider information 
                         extracted from the topology of the network, altogether 
                         disregarding additional content. In this work we propose a novel 
                         methodology to network simplification that leverages topological 
                         information and additional content associated with network 
                         elements. The proposed methodology relies on non-negative matrix 
                         factorization (NMF) and graph matching, combined to generate a 
                         hierarchical representation of the network, grouping the most 
                         similar elements in each level of the hierarchy. Moreover, the 
                         matrix factorization is only performed at the beginning of the 
                         process, reducing the computational cost without compromising the 
                         quality of the simplification. The effectiveness of the proposed 
                         methodology is assessed through a comprehensive set of 
                         quantitative evaluations and comparisons, which shows that our 
                         approach outperforms existing simplification methods.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "PID4960567.pdf",
        urlaccessdate = "2021, Jan. 25"
}


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