In recent efforts being conducted by the Social Interactome team, to validate hypotheses of the study, we have worked to make sense of the data that has been collected during two 16-week experiments and three Amazon Mechanical Turk deployments. The complexity in the data has made it challenging to discover insights/patterns. The goal of the semester was to explore newer methods to analyze the data. Through such discovery, we can test/validate hypotheses about the data, that would provide a direction for our contextual inquiry to predict attributes and behavior of participants in the study. The report and slides highlight two possible approaches that employ statistical relational learning for structure learning and network classification. Related files include data and software used during this study; results are given from the analyses undertaken.