Given a large graph with several millions or billions of nodes and edges, such as a social network, how can we explore it efficiently and find out what is in the data? In this demo we present P erseus , a large-scale system that enables the comprehensive analysis of large graphs by supporting the coupled summarization of graph properties and structures, guiding attention to outliers, and allowing the user to interactively explore normal and anomalous node behaviors. Specifically, P erseus provides for the following operations: 1) It automatically extracts graph invariants ( e.g. , degree, PageRank, real eigenvectors) by performing scalable, offline batch processing on H adoop ; 2) It interactively visualizes univariate and bivariate distributions for those invariants; 3) It summarizes the properties of the nodes that the user selects; 4) It efficiently visualizes the induced subgraph of a selected node and its neighbors, by incrementally revealing its neighbors. In our demonstration, we invite the audience to interact with P erseus to explore a variety of multi-million-edge social networks including a Wikipedia vote network, a friendship/foeship network in Slashdot, and a trust network based on the consumer review website Epinions.com.