  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 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 concept -- a temporal network where nodes are linked if
two consecutive events occur between them -- we 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. 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.

Social and Information Networks;Machine Learning;Data Analysis, Statistics and Probability;Physics and Society