Social network analysis has undergone a renaissance with the ubiquity and quantity of content from social media, web pages, and sensors. This content is a rich data source for constructing and analyzing social networks, but its enormity and unstructured nature also present multiple challenges. Work at Lincoln Laboratory is addressing the problems in constructing networks from unstructured data, analyzing the community structure of a network, and inferring information from networks. Graph analytics have proven to be valuable tools in solving these challenges. Through the use of these tools, Laboratory researchers have achieved promising results on real-world data. A sampling of these results are presented in this article.