Browsing is a fundamental aspect of exploratory information-seeking. Associative browsing represents a common and intuitive set of exploratory strategies in which users step iteratively from familiar to novel bits of information. In this paper, we examine associative browsing as a strategy for bottom-up exploration of large, heterogeneous networks. We present Refinery, an interactive visualization system informed by guidelines for associative brows- ing drawn from literature on exploratory information-seeking. These guidelines motivate Refinery’s query model, which allows users to simply and expressively construct queries using heterogeneous sets of nodes. This system computes degree-of-interest scores for associated content using a fast, random-walk algorithm. Refinery visualizes query nodes within a subgraph of results, providing explanatory context, facilitating serendipitous discovery, and stimulating continued exploration. A study of 12 academic researchers using Refinery to browse publication data demonstrates how the system enables discovery of valuable new content, even within existing areas of expertise.