  Canned patterns (i.e. small subgraph patterns) in visual graph query
interfaces (a.k.a GUI) facilitate efficient query formulation by enabling
pattern-at-a-time construction mode. However, existing GUIs for querying large
networks either do not expose any canned patterns or if they do then they are
typically selected manually based on domain knowledge. Unfortunately, manual
generation of canned patterns is not only labor intensive but may also lack
diversity for supporting efficient visual formulation of a wide range of
subgraph queries. In this paper, we present a novel generic and extensible
framework called TATTOO that takes a data-driven approach to automatically
selecting canned patterns for a GUI from large networks. Specifically, it first
decomposes the underlying network into truss-infested and truss-oblivious
regions. Then candidate canned patterns capturing different real-world query
topologies are generated from these regions. Canned patterns based on a
user-specified plug are then selected for the GUI from these candidates by
maximizing coverage and diversity, and by minimizing the cognitive load of the
pattern set. Experimental studies with real-world datasets demonstrate the
benefits of TATTOO. Importantly, this work takes a concrete step towards
realizing plug-and-play visual graph query interfaces for large networks.
