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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, probablysharing common properties and/or playing similar roles within a network. Whennetwork analysis is applied to study the livestock movement patterns, theepidemiological units of interest (farm premises, counties, states, countries,etc.) are represented as nodes, and animal movements between the nodes arerepresented as the edges of a network. Unraveling a network structure, andhence the trade preferences and pathways, could be very useful to a researcheror a decision-maker. We implemented a community detection algorithm to findlivestock communities that is consistent with the definition of a livestockproduction zone, assuming that a community is a group of farm premises in whichan 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 insidethe State of Mato Grosso, for the year of 2007. This database holds informationabout 87,899 premises and 521,431 movements throughout the year, totalizing15,844,779 animals moved. The community detection algorithm achieved a networkpartition that shows a clear geographical and commercial pattern, two crucialfeatures to preventive veterinary medicine applications, and also has ameaningful interpretation in trade networks where links emerge from the choiceof trader nodes.

Caracteriza\c{c}\~ao de circuitos pecu\'arios com base em redes de movimenta\c{c}\~ao de animais