  A network is a set of nodes that are linked together by a set of edges.
Networks can represent any set of objects that have relations among themselves.
Communities are sets of nodes that are related in an important way, probably
sharing common properties and/or playing similar roles within a network. When
network analysis is applied to study the livestock movement patterns, the
epidemiological units of interest (farm premises, counties, states, countries,
etc.) are represented as nodes, and animal movements between the nodes are
represented as the edges of a network. Unraveling a network structure, and
hence the trade preferences and pathways, could be very useful to a researcher
or a decision-maker. We implemented a community detection algorithm to find
livestock communities that is consistent with the definition of a livestock
production zone, assuming that a community is a group of farm premises in which
an animal is more likely to stay during its life time than expected by chance.
We applied this algorithm to the network of within animal movements made inside
the State of Mato Grosso, for the year of 2007. This database holds information
about 87,899 premises and 521,431 movements throughout the year, totalizing
15,844,779 animals moved. The community detection algorithm achieved a network
partition that shows a clear geographical and commercial pattern, two crucial
features to preventive veterinary medicine applications, and also has a
meaningful interpretation in trade networks where links emerge from the choice
of trader nodes.
