  Analyzing large, multivariate graphs is an important problem in many domains,
yet such graphs are challenging to visualize. In this paper, we introduce a
novel, scalable, tree+table multivariate graph visualization technique, which
makes many tasks related to multivariate graph analysis easier to achieve. The
core principle we follow is to selectively query for nodes or subgraphs of
interest and visualize these subgraphs as a spanning tree of the graph. The
tree is laid out linearly, which enables us to juxtapose the nodes with a table
visualization where diverse attributes can be shown. We also use this table as
an adjacency matrix, so that the resulting technique is a hybrid
node-link/adjacency matrix technique. We implement this concept in Juniper and
complement it with a set of interaction techniques that enable analysts to
dynamically grow, restructure, and aggregate the tree, as well as change the
layout or show paths between nodes. We demonstrate the utility of our tool in
usage scenarios for different multivariate networks: a bipartite network of
scholars, papers, and citation metrics and a multitype network of story
characters, places, books, etc.
