We study a set of algorithms to discover the community structure of networks for languages from the Americas. Our experiments are based on a parallel corpus which allows us to represent each language as a co-occurrence network. Four methods to calculate network modularity, as a measure of the quality of community structure, were used. We studied several aspects of the community structure of co-occurrence networks. First, we were able to construct the map of modularity variations across languages from the Americas. With this, we separated large groups of languages into low- and high-modularity families. We suggested also a strong influence of functional words on low-modularity languages. Finally, we found a strong relationship between word entropy values and modularity. Our approach is thus a simple network-based contribution to face data scarcity of languages which are in danger of disappearing.