Social relations are the foundation of human social life. Developing techniques to analyze such relations in visual data, such as photos, bears great potential to build machines that better understand people at a social level. Social domain-based theory from social psychology is a great starting point to systematically approach social relation recognition. The theory provides a coverage of all aspects of social relations and equally is concrete and predictive about the visual attributes and behaviors defining the relations in each social domain. We proposed the first photo dataset built on this holistic conceptualization of social life that is composed of a hierarchical label space of social domains and social relations, and contributed the first models to recognize such domains and relations and find superior performance for attribute based features. Beyond the encouraging performances, we have some findings of interpretable features that are in accordance with the predictions from social psychology literature. We interleave visual recognition and social psychology theory that has the potential to complement the theoretical work in the area with empirical and data-driven models of social life.