The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution across Twitter and Instagram using general techniques. Our methods fall into three categories: profile, content, and graph based. For the profile-based methods, we consider techniques based on approximate string matching. For content-based methods, we perform author identification. Finally, for graph-based methods, we apply novel cross-domain community detection methods and generate neighborhood-based features. The three categories of methods are applied to a large graph of users in Twitter and Instagram to understand challenges, determine performance, and understand fusion of multiple methods. Final results demonstrate an equal error rate less than 1%.