  Multivariate graphs are prolific across many fields, including transportation
and neuroscience. A key task in graph analysis is the exploration of
connectivity, to, for example, analyze how signals flow through neurons, or to
explore how well different cities are connected by flights. While standard
node-link diagrams are helpful in judging connectivity, they do not scale to
large networks. Adjacency matrices also do not scale to large networks and are
only suitable to judge connectivity of adjacent nodes. A key approach to
realize scalable graph visualization are queries: instead of displaying the
whole network, only a relevant subset is shown. Query-based techniques for
analyzing connectivity in graphs, however, can also easily suffer from
cluttering if the query result is big enough. To remedy this, we introduce
techniques that provide an overview of the connectivity and reveal details on
demand. We have two main contributions: (1) two novel visualization techniques
that work in concert for summarizing graph connectivity; and (2) Graffinity, an
open-source implementation of these visualizations supplemented by detail views
to enable a complete analysis workflow. Graffinity was designed in a close
collaboration with neuroscientists and is optimized for connectomics data
analysis, yet the technique is applicable across domains. We validate the
connectivity overview and our open-source tool with illustrative examples using
flight and connectomics data.
