  Visualization is a powerful paradigm for exploratory data analysis.
Visualizing large graphs, however, often results in a meaningless hairball. In
this paper, we propose a different approach that helps the user adaptively
explore large million-node graphs from a local perspective. For nodes that the
user investigates, we propose to only show the neighbors with the most
subjectively interesting neighborhoods. We contribute novel ideas to measure
this interestingness in terms of how surprising a neighborhood is given the
background distribution, as well as how well it fits the nodes the user chose
to explore. We introduce FACETS, a fast and scalable method for visually
exploring large graphs. By implementing our above ideas, it allows users to
look into the forest through its trees. Empirical evaluation shows that our
method works very well in practice, providing rankings of nodes that match
interests of users. Moreover, as it scales linearly, FACETS is suited for the
exploration of very large graphs.
