@InProceedings{GouvêaVeCoFeMaQu:2020:DyCoDe,
               author = "Gouv{\^e}a, Alessandra M. M. M. and Vega-Oliveros, Didier Augusto 
                         and Cotacallapa Choque, Frank Mosh{\'e} and Ferreira, Leonardo 
                         Nascimento and Macau, Elbert Einstein Nehrer and Quiles, Marcos 
                         Gon{\c{c}}alves",
          affiliation = "{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade Federal de S{\~a}o Paulo 
                         (UNIFESP)} and {Universidade Federal de S{\~a}o Paulo 
                         (UNIFESP)}",
                title = "Dynamic community detection into analyzing of wildfires events",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Gervasi, O. and Murgante, B. and Misra, S. and Garau, C. and 
                         Blecic, I. and Taniar, D. and Apduhan, B. O. and Rocha, A. M. A. 
                         C. and Tarantino, E. and Torre, C. M. and Karaca, Y.",
                pages = "1032--1047",
         organization = "International Conference on Computational Science and Its 
                         Applications (ICCSA), 20.",
            publisher = "Springer",
                 note = "Lecture Notes in Computer Science, v.12249",
             keywords = "Community detection · Temporal networks · Wildfire · Fire activity 
                         · Geographical data modeling.",
             abstract = "The study and comprehension of complex systems are crucial 
                         intellectual and scientific challenges of the 21st century. In 
                         this scenario, network science has emerged as a mathematical tool 
                         to support the study of such systems. Examples include 
                         environmental processes such as the wildfires, which are known for 
                         their considerable impact on human life. However, there is a 
                         considerable lack of studies of wildfire from a network science 
                         perspective. Here, employing the chronological network concepta 
                         temporal network where nodes are linked if two consecutive events 
                         occur between themwe investigate the information that dynamic 
                         community structures reveal about the wildfires dynamics. 
                         Particularly, we explore a two-phase dynamic community detection 
                         approach, i.e., we applied the Louvain algorithm on a series of 
                         snapshots, and then we used the Jaccard similarity coefficient to 
                         match communities across adjacent snapshots. Experiments with the 
                         MODIS dataset of fire events in the Amazon basing were conducted. 
                         Our results show that the dynamic communities can reveal wildfire 
                         patterns observed throughout the year.",
  conference-location = "Cagliari, Italy",
      conference-year = "01-04 July",
                  doi = "10.1007/978-3-030-58799-4_74",
                  url = "http://dx.doi.org/10.1007/978-3-030-58799-4_74",
                 isbn = "978-303058798-7",
                 issn = "03029743",
             language = "en",
           targetfile = "gouvea_dynamic.pdf",
        urlaccessdate = "16 jun. 2024"
}