  Graph mining to extract interesting components has been studied in various
guises, e.g., communities, dense subgraphs, cliques. However, most existing
works are based on notions of frequency and connectivity and do not capture
subjective interestingness from a user's viewpoint. Furthermore, existing
approaches to mine graphs are not interactive and cannot incorporate user
feedbacks in any natural manner. In this paper, we address these gaps by
proposing a graph maximum entropy model to discover surprising connected
subgraph patterns from entity graphs. This model is embedded in an interactive
visualization framework to enable human-in-the-loop, model-guided data
exploration. Using case studies on real datasets, we demonstrate how
interactions between users and the maximum entropy model lead to faster and
explainable conclusions.
