  The value proposition of a dataset often resides in the implicit
interconnections or explicit relationships (patterns) among individual
entities, and is often modeled as a graph. Effective visualization of such
graphs can lead to key insights uncovering such value. In this article we
propose a visualization method to explore graphs with numerical attributes
associated with nodes (or edges) -- referred to as scalar graphs. Such
numerical attributes can represent raw content information, similarities, or
derived information reflecting important network measures such as triangle
density and centrality. The proposed visualization strategy seeks to
simultaneously uncover the relationship between attribute values and graph
topology, and relies on transforming the network to generate a terrain map. A
key objective here is to ensure that the terrain map reveals the overall
distribution of components-of-interest (e.g. dense subgraphs, k-cores) and the
relationships among them while being sensitive to the attribute values over the
graph. We also design extensions that can capture the relationship across
multiple numerical attributes (scalars). We demonstrate the efficacy of our
method on several real-world data science tasks while scaling to large graphs
with millions of nodes.
